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COVID19_ICU_Prediction.ipynb
34133 lines (34133 with data), 3.0 MB
{
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"metadata": {
"colab": {
"name": "COVID19_ICU_Prediction.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "9svAv52TXlj5"
},
"source": [
"#**Machine Learning Project**\n",
"\n",
"***Title: Predicting ICU admission of confirmed COVID-19 cases***\n",
"\n",
"The COVID-19 pandemic has shown us the\n",
"unpreparedness of our current healthcare system and\n",
"services. We need to optimize the allocation of medical\n",
"resources to maximize the utilization of resources. We are\n",
"preparing this Machine Learning model based on the\n",
"clinical data of confirmed COVID-19 cases. This will help\n",
"us to predict the need of ICU for a patient in advance. By\n",
"this information hospitals can plan the flow of operations\n",
"and take critical decisions like shifting patient to another\n",
"hospital or arrangement of resources within the time so\n",
"that the lives of patients can be saved.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HYhZSsn9tXbE"
},
"source": [
"##Libraries and Packages\n",
"List of all the packages that is used in the notebook"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cvYWlx6jYN-f"
},
"source": [
"import tensorflow as tf\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.manifold import TSNE \n",
"from sklearn.decomposition import PCA \n",
"\n",
"pd.set_option('display.max_columns', None)\n"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Q52XpjzxJMR"
},
"source": [
"Downloading Dataset\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8EmfUSufxOKO",
"outputId": "06688cdc-4f08-425f-c393-e044058a8ab7"
},
"source": [
"!wget -O \"Kaggle_Sirio_Libanes_ICU_Prediction.xlsx\" \"https://drive.google.com/uc?export=download&id=1_shaH6SQajy1zrnALzim9jGaRmF3PLIn\""
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-12-20 22:41:48-- https://drive.google.com/uc?export=download&id=1_shaH6SQajy1zrnALzim9jGaRmF3PLIn\n",
"Resolving drive.google.com (drive.google.com)... 108.177.11.100, 108.177.11.113, 108.177.11.102, ...\n",
"Connecting to drive.google.com (drive.google.com)|108.177.11.100|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Moved Temporarily\n",
"Location: https://doc-0c-80-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rjbv35t6bjnqa6t8rv92guj4pokeirq3/1608504075000/04245606460885426616/*/1_shaH6SQajy1zrnALzim9jGaRmF3PLIn?e=download [following]\n",
"Warning: wildcards not supported in HTTP.\n",
"--2020-12-20 22:41:49-- https://doc-0c-80-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rjbv35t6bjnqa6t8rv92guj4pokeirq3/1608504075000/04245606460885426616/*/1_shaH6SQajy1zrnALzim9jGaRmF3PLIn?e=download\n",
"Resolving doc-0c-80-docs.googleusercontent.com (doc-0c-80-docs.googleusercontent.com)... 142.250.97.132, 2607:f8b0:400c:c18::84\n",
"Connecting to doc-0c-80-docs.googleusercontent.com (doc-0c-80-docs.googleusercontent.com)|142.250.97.132|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1047506 (1023K) [application/wps-office.xlsx]\n",
"Saving to: ‘Kaggle_Sirio_Libanes_ICU_Prediction.xlsx’\n",
"\n",
"Kaggle_Sirio_Libane 100%[===================>] 1023K --.-KB/s in 0.01s \n",
"\n",
"2020-12-20 22:41:50 (98.6 MB/s) - ‘Kaggle_Sirio_Libanes_ICU_Prediction.xlsx’ saved [1047506/1047506]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8tDv4H2mu5Z-"
},
"source": [
"##Reading Dataset\n",
"Reading the dataset from the given CSV file."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "Jw8EV6XavIT2",
"outputId": "3b8b76cb-906c-4e31-bc09-99b1f469069f"
},
"source": [
"data = pd.read_excel(\"Kaggle_Sirio_Libanes_ICU_Prediction.xlsx\")\n",
"data"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.593220</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.586207</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.237113</td>\n",
" <td>0.0000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>0.898990</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.420290</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0-2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>60th</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.333333</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.593220</td>\n",
" <td>0.535714</td>\n",
" <td>0.578947</td>\n",
" <td>0.333333</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.586207</td>\n",
" <td>0.535714</td>\n",
" <td>0.578947</td>\n",
" <td>0.443299</td>\n",
" <td>0.0000</td>\n",
" <td>-0.025641</td>\n",
" <td>-0.500000</td>\n",
" <td>0.714286</td>\n",
" <td>0.838384</td>\n",
" <td>-0.076923</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.313433</td>\n",
" <td>-0.636364</td>\n",
" <td>0.246377</td>\n",
" <td>0.578947</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>2-4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>60th</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>-1.0</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>-1.0</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-1.0</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-1.0</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-1.0</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-1.0</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4-6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>60th</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.107143</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.318681</td>\n",
" <td>0.898990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.275362</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>6-12</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>60th</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-1.0</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-1.0</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>-1.0</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-1.0</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-1.0</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-1.0</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-1.0</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-1.0</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-1.0</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-1.0</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-1.0</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-1.0</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-1.0</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>-1.0</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>-1.0</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-1.0</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-1.0</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>-1.0</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
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" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
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" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-1.0</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
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" <td>-0.996762</td>\n",
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" <td>-0.996762</td>\n",
" <td>-1.0</td>\n",
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" <td>0.035714</td>\n",
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" <td>-0.339130</td>\n",
" <td>0.325153</td>\n",
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" <td>0.096774</td>\n",
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" <td>-0.814433</td>\n",
" <td>ABOVE_12</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1920</th>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" <td>50th</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.012346</td>\n",
" <td>-0.292308</td>\n",
" <td>0.056604</td>\n",
" <td>-0.525424</td>\n",
" <td>0.535714</td>\n",
" <td>0.789474</td>\n",
" <td>0.012346</td>\n",
" <td>-0.292308</td>\n",
" <td>0.056604</td>\n",
" <td>-0.517241</td>\n",
" <td>0.535714</td>\n",
" <td>0.789474</td>\n",
" <td>0.175258</td>\n",
" <td>-0.0500</td>\n",
" <td>0.145299</td>\n",
" <td>-0.428571</td>\n",
" <td>0.714286</td>\n",
" <td>0.919192</td>\n",
" <td>-0.299145</td>\n",
" <td>-0.502703</td>\n",
" <td>-0.164179</td>\n",
" <td>-0.575758</td>\n",
" <td>0.246377</td>\n",
" <td>0.789474</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0-2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1921</th>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" <td>50th</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.717277</td>\n",
" <td>-0.717277</td>\n",
" <td>-0.717277</td>\n",
" <td>-0.717277</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.982208</td>\n",
" <td>-0.982208</td>\n",
" <td>-0.982208</td>\n",
" <td>-0.982208</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.244898</td>\n",
" <td>0.244898</td>\n",
" <td>0.244898</td>\n",
" <td>0.244898</td>\n",
" <td>-1.0</td>\n",
" <td>-0.934890</td>\n",
" <td>-0.934890</td>\n",
" <td>-0.934890</td>\n",
" <td>-0.934890</td>\n",
" <td>-1.0</td>\n",
" <td>-0.782516</td>\n",
" <td>-0.782516</td>\n",
" <td>-0.782516</td>\n",
" <td>-0.782516</td>\n",
" <td>-1.0</td>\n",
" <td>-0.960280</td>\n",
" <td>-0.960280</td>\n",
" <td>-0.960280</td>\n",
" <td>-0.960280</td>\n",
" <td>-1.0</td>\n",
" <td>-0.862197</td>\n",
" <td>-0.862197</td>\n",
" <td>-0.862197</td>\n",
" <td>-0.862197</td>\n",
" <td>-1.0</td>\n",
" <td>-0.064990</td>\n",
" <td>-0.064990</td>\n",
" <td>-0.064990</td>\n",
" <td>-0.064990</td>\n",
" <td>-1.0</td>\n",
" <td>-0.158537</td>\n",
" <td>-0.158537</td>\n",
" <td>-0.158537</td>\n",
" <td>-0.158537</td>\n",
" <td>-1.0</td>\n",
" <td>-0.957340</td>\n",
" <td>-0.957340</td>\n",
" <td>-0.957340</td>\n",
" <td>-0.957340</td>\n",
" <td>-1.0</td>\n",
" <td>-0.897773</td>\n",
" <td>-0.897773</td>\n",
" <td>-0.897773</td>\n",
" <td>-0.897773</td>\n",
" <td>-1.0</td>\n",
" <td>-0.848590</td>\n",
" <td>-0.848590</td>\n",
" <td>-0.848590</td>\n",
" <td>-0.848590</td>\n",
" <td>-1.0</td>\n",
" <td>-0.686722</td>\n",
" <td>-0.686722</td>\n",
" <td>-0.686722</td>\n",
" <td>-0.686722</td>\n",
" <td>-1.0</td>\n",
" <td>-0.913165</td>\n",
" <td>-0.913165</td>\n",
" <td>-0.913165</td>\n",
" <td>-0.913165</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.857988</td>\n",
" <td>-0.857988</td>\n",
" <td>-0.857988</td>\n",
" <td>-0.857988</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.730061</td>\n",
" <td>-1.0</td>\n",
" <td>-0.906238</td>\n",
" <td>-0.906238</td>\n",
" <td>-0.906238</td>\n",
" <td>-0.906238</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.424242</td>\n",
" <td>0.424242</td>\n",
" <td>0.424242</td>\n",
" <td>0.424242</td>\n",
" <td>-1.0</td>\n",
" <td>-0.479306</td>\n",
" <td>-0.479306</td>\n",
" <td>-0.479306</td>\n",
" <td>-0.479306</td>\n",
" <td>-1.0</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.333333</td>\n",
" <td>-1.0</td>\n",
" <td>-0.085714</td>\n",
" <td>-0.085714</td>\n",
" <td>-0.085714</td>\n",
" <td>-0.085714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997387</td>\n",
" <td>-0.997387</td>\n",
" <td>-0.997387</td>\n",
" <td>-0.997387</td>\n",
" <td>-1.0</td>\n",
" <td>-0.992378</td>\n",
" <td>-0.992378</td>\n",
" <td>-0.992378</td>\n",
" <td>-0.992378</td>\n",
" <td>-1.0</td>\n",
" <td>-0.869210</td>\n",
" <td>-0.869210</td>\n",
" <td>-0.869210</td>\n",
" <td>-0.869210</td>\n",
" <td>-1.0</td>\n",
" <td>-0.879518</td>\n",
" <td>-0.879518</td>\n",
" <td>-0.879518</td>\n",
" <td>-0.879518</td>\n",
" <td>-1.0</td>\n",
" <td>-0.979571</td>\n",
" <td>-0.979571</td>\n",
" <td>-0.979571</td>\n",
" <td>-0.979571</td>\n",
" <td>-1.0</td>\n",
" <td>0.086420</td>\n",
" <td>-0.384615</td>\n",
" <td>-0.113208</td>\n",
" <td>-0.593220</td>\n",
" <td>0.142857</td>\n",
" <td>0.578947</td>\n",
" <td>0.086420</td>\n",
" <td>-0.384615</td>\n",
" <td>-0.113208</td>\n",
" <td>-0.586207</td>\n",
" <td>0.142857</td>\n",
" <td>0.578947</td>\n",
" <td>0.237113</td>\n",
" <td>-0.1250</td>\n",
" <td>-0.008547</td>\n",
" <td>-0.500000</td>\n",
" <td>0.472527</td>\n",
" <td>0.838384</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.567568</td>\n",
" <td>-0.298507</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.072464</td>\n",
" <td>0.578947</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>2-4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1922</th>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" <td>50th</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.169811</td>\n",
" <td>-0.593220</td>\n",
" <td>0.142857</td>\n",
" <td>0.736842</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.169811</td>\n",
" <td>-0.586207</td>\n",
" <td>0.142857</td>\n",
" <td>0.736842</td>\n",
" <td>0.237113</td>\n",
" <td>0.0000</td>\n",
" <td>-0.059829</td>\n",
" <td>-0.500000</td>\n",
" <td>0.472527</td>\n",
" <td>0.898990</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.343284</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.072464</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>4-6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1923</th>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" <td>50th</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.209877</td>\n",
" <td>-0.384615</td>\n",
" <td>-0.188679</td>\n",
" <td>-0.661017</td>\n",
" <td>0.285714</td>\n",
" <td>0.473684</td>\n",
" <td>0.209877</td>\n",
" <td>-0.384615</td>\n",
" <td>-0.188679</td>\n",
" <td>-0.655172</td>\n",
" <td>0.285714</td>\n",
" <td>0.473684</td>\n",
" <td>0.340206</td>\n",
" <td>-0.1250</td>\n",
" <td>-0.076923</td>\n",
" <td>-0.571429</td>\n",
" <td>0.560440</td>\n",
" <td>0.797980</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.567568</td>\n",
" <td>-0.358209</td>\n",
" <td>-0.696970</td>\n",
" <td>0.043478</td>\n",
" <td>0.473684</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>6-12</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1924</th>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" <td>50th</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.983255</td>\n",
" <td>-0.983255</td>\n",
" <td>-0.983255</td>\n",
" <td>-0.983255</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.306122</td>\n",
" <td>0.306122</td>\n",
" <td>0.306122</td>\n",
" <td>0.306122</td>\n",
" <td>-1.0</td>\n",
" <td>-0.944798</td>\n",
" <td>-0.944798</td>\n",
" <td>-0.944798</td>\n",
" <td>-0.944798</td>\n",
" <td>-1.0</td>\n",
" <td>-0.825160</td>\n",
" <td>-0.825160</td>\n",
" <td>-0.825160</td>\n",
" <td>-0.825160</td>\n",
" <td>-1.0</td>\n",
" <td>-0.962617</td>\n",
" <td>-0.962617</td>\n",
" <td>-0.962617</td>\n",
" <td>-0.962617</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>-0.157233</td>\n",
" <td>-0.157233</td>\n",
" <td>-0.157233</td>\n",
" <td>-0.157233</td>\n",
" <td>-1.0</td>\n",
" <td>-0.292683</td>\n",
" <td>-0.292683</td>\n",
" <td>-0.292683</td>\n",
" <td>-0.292683</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.850521</td>\n",
" <td>-0.850521</td>\n",
" <td>-0.850521</td>\n",
" <td>-0.850521</td>\n",
" <td>-1.0</td>\n",
" <td>-0.634855</td>\n",
" <td>-0.634855</td>\n",
" <td>-0.634855</td>\n",
" <td>-0.634855</td>\n",
" <td>-1.0</td>\n",
" <td>-0.935974</td>\n",
" <td>-0.935974</td>\n",
" <td>-0.935974</td>\n",
" <td>-0.935974</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.801134</td>\n",
" <td>-0.801134</td>\n",
" <td>-0.801134</td>\n",
" <td>-0.801134</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.463284</td>\n",
" <td>-0.463284</td>\n",
" <td>-0.463284</td>\n",
" <td>-0.463284</td>\n",
" <td>-1.0</td>\n",
" <td>-0.444444</td>\n",
" <td>-0.444444</td>\n",
" <td>-0.444444</td>\n",
" <td>-0.444444</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997761</td>\n",
" <td>-0.997761</td>\n",
" <td>-0.997761</td>\n",
" <td>-0.997761</td>\n",
" <td>-1.0</td>\n",
" <td>-0.991997</td>\n",
" <td>-0.991997</td>\n",
" <td>-0.991997</td>\n",
" <td>-0.991997</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.807229</td>\n",
" <td>-0.807229</td>\n",
" <td>-0.807229</td>\n",
" <td>-0.807229</td>\n",
" <td>-1.0</td>\n",
" <td>-0.888448</td>\n",
" <td>-0.888448</td>\n",
" <td>-0.888448</td>\n",
" <td>-0.888448</td>\n",
" <td>-1.0</td>\n",
" <td>-0.185185</td>\n",
" <td>-0.539103</td>\n",
" <td>-0.107704</td>\n",
" <td>-0.610169</td>\n",
" <td>0.050595</td>\n",
" <td>0.662281</td>\n",
" <td>-0.160494</td>\n",
" <td>-0.538462</td>\n",
" <td>-0.075472</td>\n",
" <td>-0.586207</td>\n",
" <td>0.071429</td>\n",
" <td>0.631579</td>\n",
" <td>-0.175258</td>\n",
" <td>-0.3750</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.785714</td>\n",
" <td>0.186813</td>\n",
" <td>0.777778</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.470270</td>\n",
" <td>-0.149254</td>\n",
" <td>-0.515152</td>\n",
" <td>0.101449</td>\n",
" <td>0.842105</td>\n",
" <td>-0.652174</td>\n",
" <td>-0.644172</td>\n",
" <td>-0.633588</td>\n",
" <td>-0.647059</td>\n",
" <td>-0.547619</td>\n",
" <td>-0.838384</td>\n",
" <td>-0.701863</td>\n",
" <td>-0.585967</td>\n",
" <td>-0.763868</td>\n",
" <td>-0.612903</td>\n",
" <td>-0.551337</td>\n",
" <td>-0.835052</td>\n",
" <td>ABOVE_12</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1925 rows × 231 columns</p>\n",
"</div>"
],
"text/plain": [
" PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n",
"0 0 1 60th 0 \n",
"1 0 1 60th 0 \n",
"2 0 1 60th 0 \n",
"3 0 1 60th 0 \n",
"4 0 1 60th 0 \n",
"... ... ... ... ... \n",
"1920 384 0 50th 1 \n",
"1921 384 0 50th 1 \n",
"1922 384 0 50th 1 \n",
"1923 384 0 50th 1 \n",
"1924 384 0 50th 1 \n",
"\n",
" DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"1920 0.0 0.0 0.0 \n",
"1921 0.0 0.0 0.0 \n",
"1922 0.0 0.0 0.0 \n",
"1923 0.0 0.0 0.0 \n",
"1924 0.0 0.0 1.0 \n",
"\n",
" DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 HTN \\\n",
"0 0.0 1.0 1.0 0.0 \n",
"1 0.0 1.0 1.0 0.0 \n",
"2 0.0 1.0 1.0 0.0 \n",
"3 0.0 1.0 1.0 0.0 \n",
"4 0.0 1.0 1.0 0.0 \n",
"... ... ... ... ... \n",
"1920 0.0 0.0 0.0 0.0 \n",
"1921 0.0 0.0 0.0 0.0 \n",
"1922 0.0 0.0 0.0 0.0 \n",
"1923 0.0 0.0 0.0 0.0 \n",
"1924 0.0 0.0 0.0 0.0 \n",
"\n",
" IMMUNOCOMPROMISED OTHER ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN \\\n",
"0 0.0 1.0 NaN NaN NaN \n",
"1 0.0 1.0 NaN NaN NaN \n",
"2 0.0 1.0 0.605263 0.605263 0.605263 \n",
"3 0.0 1.0 NaN NaN NaN \n",
"4 0.0 1.0 0.000000 0.000000 0.000000 \n",
"... ... ... ... ... ... \n",
"1920 0.0 1.0 NaN NaN NaN \n",
"1921 0.0 1.0 0.605263 0.605263 0.605263 \n",
"1922 0.0 1.0 NaN NaN NaN \n",
"1923 0.0 1.0 NaN NaN NaN \n",
"1924 0.0 1.0 0.605263 0.605263 0.605263 \n",
"\n",
" ALBUMIN_MAX ALBUMIN_DIFF BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.605263 -1.0 -1.000000 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 0.000000 -1.0 -0.871658 -0.871658 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 0.605263 -1.0 -1.000000 -1.000000 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 0.605263 -1.0 -1.000000 -1.000000 \n",
"\n",
" BE_ARTERIAL_MIN BE_ARTERIAL_MAX BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.000000 -1.0 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.871658 -0.871658 -1.0 -0.863874 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -1.000000 -1.000000 -1.0 -0.717277 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -1.000000 -1.000000 -1.0 -1.000000 \n",
"\n",
" BE_VENOUS_MEAN BE_VENOUS_MIN BE_VENOUS_MAX BE_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.000000 -1.000000 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.863874 -0.863874 -0.863874 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.717277 -0.717277 -0.717277 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -1.000000 -1.000000 -1.000000 -1.0 \n",
"\n",
" BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN BIC_ARTERIAL_MIN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.317073 -0.317073 -0.317073 \n",
"3 NaN NaN NaN \n",
"4 -0.317073 -0.317073 -0.317073 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.317073 -0.317073 -0.317073 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.317073 -0.317073 -0.317073 \n",
"\n",
" BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN BIC_VENOUS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -1.0 -0.317073 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.317073 -1.0 -0.414634 -0.414634 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.317073 -1.0 -0.170732 -0.170732 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.317073 -1.0 -0.317073 -0.317073 \n",
"\n",
" BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF BILLIRUBIN_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -1.0 -0.938950 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.414634 -0.414634 -1.0 -0.979069 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.170732 -0.170732 -1.0 -0.982208 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.317073 -0.317073 -1.0 -0.983255 \n",
"\n",
" BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX BILLIRUBIN_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.938950 -0.938950 -0.938950 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.979069 -0.979069 -0.979069 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.982208 -0.982208 -0.982208 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.983255 -0.983255 -0.983255 -1.0 \n",
"\n",
" BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX BLAST_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN \n",
"1921 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"1922 NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN \n",
"1924 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX CALCIUM_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 0.183673 0.183673 0.183673 0.183673 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.326531 0.326531 0.326531 0.326531 -1.0 \n",
"... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN \n",
"1921 0.244898 0.244898 0.244898 0.244898 -1.0 \n",
"1922 NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN \n",
"1924 0.306122 0.306122 0.306122 0.306122 -1.0 \n",
"\n",
" CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN CREATININ_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.868365 -0.868365 -0.868365 -0.868365 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.926398 -0.926398 -0.926398 -0.926398 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.934890 -0.934890 -0.934890 -0.934890 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.944798 -0.944798 -0.944798 -0.944798 \n",
"\n",
" CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX FFA_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.859275 -0.859275 -0.859275 -0.859275 -1.0 \n",
"... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN \n",
"1921 -1.0 -0.782516 -0.782516 -0.782516 -0.782516 -1.0 \n",
"1922 NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN \n",
"1924 -1.0 -0.825160 -0.825160 -0.825160 -0.825160 -1.0 \n",
"\n",
" GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF GLUCOSE_MEDIAN \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 -0.891993 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.669393 -0.669393 -0.669393 -0.669393 -1.0 -0.891993 \n",
"... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN \n",
"1921 -0.960280 -0.960280 -0.960280 -0.960280 -1.0 -0.862197 \n",
"1922 NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN \n",
"1924 -0.962617 -0.962617 -0.962617 -0.962617 -1.0 -0.891993 \n",
"\n",
" GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.891993 -0.891993 -0.891993 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.891993 -0.891993 -0.891993 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.862197 -0.862197 -0.862197 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.891993 -0.891993 -0.891993 -1.0 \n",
"\n",
" HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.090147 0.090147 0.090147 0.090147 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.320755 -0.320755 -0.320755 -0.320755 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.064990 -0.064990 -0.064990 -0.064990 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.157233 -0.157233 -0.157233 -0.157233 \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.109756 0.109756 0.109756 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.353659 -0.353659 -0.353659 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -1.0 -0.158537 -0.158537 -0.158537 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -1.0 -0.292683 -0.292683 -0.292683 \n",
"\n",
" HEMOGLOBIN_MAX HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 0.109756 -1.0 -0.932246 -0.932246 -0.932246 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.353659 -1.0 -0.979925 -0.979925 -0.979925 \n",
"... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN \n",
"1921 -0.158537 -1.0 -0.957340 -0.957340 -0.957340 \n",
"1922 NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN \n",
"1924 -0.292683 -1.0 -0.959849 -0.959849 -0.959849 \n",
"\n",
" INR_MAX INR_DIFF LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.932246 -1.0 1.000000 1.000000 1.000000 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.979925 -1.0 -0.963023 -0.963023 -0.963023 \n",
"... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN \n",
"1921 -0.957340 -1.0 -0.897773 -0.897773 -0.897773 \n",
"1922 NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN \n",
"1924 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" LACTATE_MAX LACTATE_DIFF LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 1.000000 -1.0 -0.835844 -0.835844 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.963023 -1.0 -0.762843 -0.762843 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.897773 -1.0 -0.848590 -0.848590 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 1.000000 -1.0 -0.850521 -0.850521 \n",
"\n",
" LEUKOCYTES_MIN LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.835844 -0.835844 -1.0 -0.914938 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.762843 -0.762843 -1.0 -0.643154 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.848590 -0.848590 -1.0 -0.686722 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.850521 -0.850521 -1.0 -0.634855 \n",
"\n",
" LINFOCITOS_MEAN LINFOCITOS_MIN LINFOCITOS_MAX LINFOCITOS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.914938 -0.914938 -0.914938 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.643154 -0.643154 -0.643154 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.686722 -0.686722 -0.686722 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.634855 -0.634855 -0.634855 -1.0 \n",
"\n",
" NEUTROPHILES_MEDIAN NEUTROPHILES_MEAN NEUTROPHILES_MIN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.868747 -0.868747 -0.868747 \n",
"3 NaN NaN NaN \n",
"4 -0.868747 -0.868747 -0.868747 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.913165 -0.913165 -0.913165 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.935974 -0.935974 -0.935974 \n",
"\n",
" NEUTROPHILES_MAX NEUTROPHILES_DIFF P02_ARTERIAL_MEDIAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.868747 -1.0 -0.170732 \n",
"3 NaN NaN NaN \n",
"4 -0.868747 -1.0 -0.365854 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.913165 -1.0 -0.170732 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.935974 -1.0 -0.170732 \n",
"\n",
" P02_ARTERIAL_MEAN P02_ARTERIAL_MIN P02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.170732 -0.170732 -0.170732 \n",
"3 NaN NaN NaN \n",
"4 -0.365854 -0.365854 -0.365854 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.170732 -0.170732 -0.170732 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.170732 -0.170732 -0.170732 \n",
"\n",
" P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.704142 -0.704142 -0.704142 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.230769 -0.230769 -0.230769 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -1.0 -0.857988 -0.857988 -0.857988 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -1.0 -0.704142 -0.704142 -0.704142 \n",
"\n",
" P02_VENOUS_MAX P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.704142 -1.0 -0.779310 \n",
"3 NaN NaN NaN \n",
"4 -0.230769 -1.0 -0.875862 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.857988 -1.0 -0.779310 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.704142 -1.0 -0.779310 \n",
"\n",
" PC02_ARTERIAL_MEAN PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.779310 -0.779310 -0.779310 \n",
"3 NaN NaN NaN \n",
"4 -0.875862 -0.875862 -0.875862 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -0.779310 -0.779310 -0.779310 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -0.779310 -0.779310 -0.779310 \n",
"\n",
" PC02_ARTERIAL_DIFF PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -1.0 -0.754601 -0.754601 \n",
"3 NaN NaN NaN \n",
"4 -1.0 -0.815951 -0.815951 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -1.0 -0.730061 -0.730061 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -1.0 -0.754601 -0.754601 \n",
"\n",
" PC02_VENOUS_MIN PC02_VENOUS_MAX PC02_VENOUS_DIFF PCR_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.754601 -0.754601 -1.0 -0.875236 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.815951 -0.815951 -1.0 -1.000000 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.730061 -0.730061 -1.0 -0.906238 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.754601 -0.754601 -1.0 -0.801134 \n",
"\n",
" PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF PH_ARTERIAL_MEDIAN \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.875236 -0.875236 -0.875236 -1.0 0.234043 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.000000 -1.000000 -1.000000 -1.0 0.574468 \n",
"... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN \n",
"1921 -0.906238 -0.906238 -0.906238 -1.0 0.234043 \n",
"1922 NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN \n",
"1924 -0.801134 -0.801134 -0.801134 -1.0 0.234043 \n",
"\n",
" PH_ARTERIAL_MEAN PH_ARTERIAL_MIN PH_ARTERIAL_MAX PH_ARTERIAL_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.234043 0.234043 0.234043 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 0.574468 0.574468 0.574468 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 0.234043 0.234043 0.234043 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 0.234043 0.234043 0.234043 -1.0 \n",
"\n",
" PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN PH_VENOUS_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.363636 0.363636 0.363636 0.363636 \n",
"3 NaN NaN NaN NaN \n",
"4 0.393939 0.393939 0.393939 0.393939 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 0.424242 0.424242 0.424242 0.424242 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 0.363636 0.363636 0.363636 0.363636 \n",
"\n",
" PH_VENOUS_DIFF PLATELETS_MEDIAN PLATELETS_MEAN PLATELETS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.540721 -0.540721 -0.540721 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.471295 -0.471295 -0.471295 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -1.0 -0.479306 -0.479306 -0.479306 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -1.0 -0.463284 -0.463284 -0.463284 \n",
"\n",
" PLATELETS_MAX PLATELETS_DIFF POTASSIUM_MEDIAN POTASSIUM_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.540721 -1.0 -0.518519 -0.518519 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.471295 -1.0 -0.666667 -0.666667 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.479306 -1.0 -0.333333 -0.333333 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.463284 -1.0 -0.444444 -0.444444 \n",
"\n",
" POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF SAT02_ARTERIAL_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.518519 -0.518519 -1.0 0.939394 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.666667 -0.666667 -1.0 0.848485 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.333333 -0.333333 -1.0 0.939394 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.444444 -0.444444 -1.0 0.939394 \n",
"\n",
" SAT02_ARTERIAL_MEAN SAT02_ARTERIAL_MIN SAT02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 0.939394 0.939394 \n",
"3 NaN NaN NaN \n",
"4 0.848485 0.848485 0.848485 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 0.939394 0.939394 0.939394 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 0.939394 0.939394 0.939394 \n",
"\n",
" SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN SAT02_VENOUS_MEAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -1.0 0.345679 0.345679 \n",
"3 NaN NaN NaN \n",
"4 -1.0 0.925926 0.925926 \n",
"... ... ... ... \n",
"1920 NaN NaN NaN \n",
"1921 -1.0 -0.333333 -0.333333 \n",
"1922 NaN NaN NaN \n",
"1923 NaN NaN NaN \n",
"1924 -1.0 0.345679 0.345679 \n",
"\n",
" SAT02_VENOUS_MIN SAT02_VENOUS_MAX SAT02_VENOUS_DIFF SODIUM_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.345679 0.345679 -1.0 -0.028571 \n",
"3 NaN NaN NaN NaN \n",
"4 0.925926 0.925926 -1.0 0.142857 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.333333 -0.333333 -1.0 -0.085714 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 0.345679 0.345679 -1.0 0.200000 \n",
"\n",
" SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF TGO_MEDIAN TGO_MEAN \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.028571 -0.028571 -0.028571 -1.0 -0.997201 -0.997201 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 0.142857 0.142857 0.142857 -1.0 -0.999067 -0.999067 \n",
"... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN \n",
"1921 -0.085714 -0.085714 -0.085714 -1.0 -0.997387 -0.997387 \n",
"1922 NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN \n",
"1924 0.200000 0.200000 0.200000 -1.0 -0.997761 -0.997761 \n",
"\n",
" TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN TGP_MIN TGP_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.997201 -0.997201 -1.0 -0.990854 -0.990854 -0.990854 -0.990854 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.999067 -0.999067 -1.0 -0.983994 -0.983994 -0.983994 -0.983994 \n",
"... ... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN NaN \n",
"1921 -0.997387 -0.997387 -1.0 -0.992378 -0.992378 -0.992378 -0.992378 \n",
"1922 NaN NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN NaN \n",
"1924 -0.997761 -0.997761 -1.0 -0.991997 -0.991997 -0.991997 -0.991997 \n",
"\n",
" TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX TTPA_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.825613 -0.825613 -0.825613 -0.825613 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 \n",
"... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN \n",
"1921 -1.0 -0.869210 -0.869210 -0.869210 -0.869210 -1.0 \n",
"1922 NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN \n",
"1924 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 \n",
"\n",
" UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF DIMER_MEDIAN \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 -0.994912 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 -0.996762 \n",
"... ... ... ... ... ... ... \n",
"1920 NaN NaN NaN NaN NaN NaN \n",
"1921 -0.879518 -0.879518 -0.879518 -0.879518 -1.0 -0.979571 \n",
"1922 NaN NaN NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN NaN NaN \n",
"1924 -0.807229 -0.807229 -0.807229 -0.807229 -1.0 -0.888448 \n",
"\n",
" DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.994912 -0.994912 -0.994912 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.996762 -0.996762 -0.996762 -1.0 \n",
"... ... ... ... ... \n",
"1920 NaN NaN NaN NaN \n",
"1921 -0.979571 -0.979571 -0.979571 -1.0 \n",
"1922 NaN NaN NaN NaN \n",
"1923 NaN NaN NaN NaN \n",
"1924 -0.888448 -0.888448 -0.888448 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN \\\n",
"0 0.086420 -0.230769 \n",
"1 0.333333 -0.230769 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 -0.243021 -0.338537 \n",
"... ... ... \n",
"1920 0.012346 -0.292308 \n",
"1921 0.086420 -0.384615 \n",
"1922 0.086420 -0.230769 \n",
"1923 0.209877 -0.384615 \n",
"1924 -0.185185 -0.539103 \n",
"\n",
" HEART_RATE_MEAN RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN \\\n",
"0 -0.283019 -0.593220 -0.285714 \n",
"1 -0.132075 -0.593220 0.535714 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN -0.107143 \n",
"4 -0.213031 -0.317859 0.033779 \n",
"... ... ... ... \n",
"1920 0.056604 -0.525424 0.535714 \n",
"1921 -0.113208 -0.593220 0.142857 \n",
"1922 -0.169811 -0.593220 0.142857 \n",
"1923 -0.188679 -0.661017 0.285714 \n",
"1924 -0.107704 -0.610169 0.050595 \n",
"\n",
" OXYGEN_SATURATION_MEAN BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"0 0.736842 0.086420 \n",
"1 0.578947 0.333333 \n",
"2 NaN NaN \n",
"3 0.736842 NaN \n",
"4 0.665932 -0.283951 \n",
"... ... ... \n",
"1920 0.789474 0.012346 \n",
"1921 0.578947 0.086420 \n",
"1922 0.736842 0.086420 \n",
"1923 0.473684 0.209877 \n",
"1924 0.662281 -0.160494 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN \\\n",
"0 -0.230769 -0.283019 \n",
"1 -0.230769 -0.132075 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 -0.376923 -0.188679 \n",
"... ... ... \n",
"1920 -0.292308 0.056604 \n",
"1921 -0.384615 -0.113208 \n",
"1922 -0.230769 -0.169811 \n",
"1923 -0.384615 -0.188679 \n",
"1924 -0.538462 -0.075472 \n",
"\n",
" RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN OXYGEN_SATURATION_MEDIAN \\\n",
"0 -0.586207 -0.285714 0.736842 \n",
"1 -0.586207 0.535714 0.578947 \n",
"2 NaN NaN NaN \n",
"3 NaN -0.107143 0.736842 \n",
"4 -0.379310 0.035714 0.631579 \n",
"... ... ... ... \n",
"1920 -0.517241 0.535714 0.789474 \n",
"1921 -0.586207 0.142857 0.578947 \n",
"1922 -0.586207 0.142857 0.736842 \n",
"1923 -0.655172 0.285714 0.473684 \n",
"1924 -0.586207 0.071429 0.631579 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MIN BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN \\\n",
"0 0.237113 0.0000 -0.162393 \n",
"1 0.443299 0.0000 -0.025641 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.340206 -0.4875 -0.572650 \n",
"... ... ... ... \n",
"1920 0.175258 -0.0500 0.145299 \n",
"1921 0.237113 -0.1250 -0.008547 \n",
"1922 0.237113 0.0000 -0.059829 \n",
"1923 0.340206 -0.1250 -0.076923 \n",
"1924 -0.175258 -0.3750 -0.247863 \n",
"\n",
" RESPIRATORY_RATE_MIN TEMPERATURE_MIN OXYGEN_SATURATION_MIN \\\n",
"0 -0.500000 0.208791 0.898990 \n",
"1 -0.500000 0.714286 0.838384 \n",
"2 NaN NaN NaN \n",
"3 NaN 0.318681 0.898990 \n",
"4 -0.857143 0.098901 0.797980 \n",
"... ... ... ... \n",
"1920 -0.428571 0.714286 0.919192 \n",
"1921 -0.500000 0.472527 0.838384 \n",
"1922 -0.500000 0.472527 0.898990 \n",
"1923 -0.571429 0.560440 0.797980 \n",
"1924 -0.785714 0.186813 0.777778 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MAX BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX \\\n",
"0 -0.247863 -0.459459 -0.432836 \n",
"1 -0.076923 -0.459459 -0.313433 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.076923 0.286486 0.298507 \n",
"... ... ... ... \n",
"1920 -0.299145 -0.502703 -0.164179 \n",
"1921 -0.247863 -0.567568 -0.298507 \n",
"1922 -0.247863 -0.459459 -0.343284 \n",
"1923 -0.162393 -0.567568 -0.358209 \n",
"1924 -0.247863 -0.470270 -0.149254 \n",
"\n",
" RESPIRATORY_RATE_MAX TEMPERATURE_MAX OXYGEN_SATURATION_MAX \\\n",
"0 -0.636364 -0.420290 0.736842 \n",
"1 -0.636364 0.246377 0.578947 \n",
"2 NaN NaN NaN \n",
"3 NaN -0.275362 0.736842 \n",
"4 0.272727 0.362319 0.947368 \n",
"... ... ... ... \n",
"1920 -0.575758 0.246377 0.789474 \n",
"1921 -0.636364 -0.072464 0.578947 \n",
"1922 -0.636364 -0.072464 0.736842 \n",
"1923 -0.696970 0.043478 0.473684 \n",
"1924 -0.515152 0.101449 0.842105 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF BLOODPRESSURE_SISTOLIC_DIFF \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 -0.339130 0.325153 \n",
"... ... ... \n",
"1920 -1.000000 -1.000000 \n",
"1921 -1.000000 -1.000000 \n",
"1922 -1.000000 -1.000000 \n",
"1923 -1.000000 -1.000000 \n",
"1924 -0.652174 -0.644172 \n",
"\n",
" HEART_RATE_DIFF RESPIRATORY_RATE_DIFF TEMPERATURE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN -1.000000 \n",
"4 0.114504 0.176471 -0.238095 \n",
"... ... ... ... \n",
"1920 -1.000000 -1.000000 -1.000000 \n",
"1921 -1.000000 -1.000000 -1.000000 \n",
"1922 -1.000000 -1.000000 -1.000000 \n",
"1923 -1.000000 -1.000000 -1.000000 \n",
"1924 -0.633588 -0.647059 -0.547619 \n",
"\n",
" OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 -1.000000 NaN \n",
"4 -0.818182 -0.389967 \n",
"... ... ... \n",
"1920 -1.000000 -1.000000 \n",
"1921 -1.000000 -1.000000 \n",
"1922 -1.000000 -1.000000 \n",
"1923 -1.000000 -1.000000 \n",
"1924 -0.838384 -0.701863 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 0.407558 -0.230462 \n",
"... ... ... \n",
"1920 -1.000000 -1.000000 \n",
"1921 -1.000000 -1.000000 \n",
"1922 -1.000000 -1.000000 \n",
"1923 -1.000000 -1.000000 \n",
"1924 -0.585967 -0.763868 \n",
"\n",
" RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN -1.000000 \n",
"4 0.096774 -0.242282 \n",
"... ... ... \n",
"1920 -1.000000 -1.000000 \n",
"1921 -1.000000 -1.000000 \n",
"1922 -1.000000 -1.000000 \n",
"1923 -1.000000 -1.000000 \n",
"1924 -0.612903 -0.551337 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL WINDOW ICU \n",
"0 -1.000000 0-2 0 \n",
"1 -1.000000 2-4 0 \n",
"2 NaN 4-6 0 \n",
"3 -1.000000 6-12 0 \n",
"4 -0.814433 ABOVE_12 1 \n",
"... ... ... ... \n",
"1920 -1.000000 0-2 0 \n",
"1921 -1.000000 2-4 0 \n",
"1922 -1.000000 4-6 0 \n",
"1923 -1.000000 6-12 0 \n",
"1924 -0.835052 ABOVE_12 0 \n",
"\n",
"[1925 rows x 231 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sGhc7h-hOXTz"
},
"source": [
"##Data Pre-Processing\n",
"Converting the data into usable format.\n",
"Following modifications has been done to the data to get most out of it:\n",
"1. Binary hotcoding to convert not float columns.\n",
"2. Marking Window 0-2 as 1 if the patient was admitted to ICU in any of the future windows. \n",
"3. Removing all the records of the windows in which patients were actually admitted to the ICU (windows with ICU label 1 before the step 2).\n",
"4. Filling the NaN values of window 0-2 with the help of mean of values in all the windows of that patient.\n",
"5. Removing all the rows still having NaN values.\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 603
},
"id": "zoW7Q52iOdlx",
"outputId": "63c87a59-2850-4b79-a126-b0843ef8bf19"
},
"source": [
"print(data.dtypes)\n",
"data.select_dtypes(object)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"PATIENT_VISIT_IDENTIFIER int64\n",
"AGE_ABOVE65 int64\n",
"AGE_PERCENTIL object\n",
"GENDER int64\n",
"DISEASE GROUPING 1 float64\n",
" ... \n",
"RESPIRATORY_RATE_DIFF_REL float64\n",
"TEMPERATURE_DIFF_REL float64\n",
"OXYGEN_SATURATION_DIFF_REL float64\n",
"WINDOW object\n",
"ICU int64\n",
"Length: 231, dtype: object\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_PERCENTIL</th>\n",
" <th>WINDOW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>60th</td>\n",
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" <td>60th</td>\n",
" <td>4-6</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>60th</td>\n",
" <td>6-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>60th</td>\n",
" <td>ABOVE_12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1920</th>\n",
" <td>50th</td>\n",
" <td>0-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1921</th>\n",
" <td>50th</td>\n",
" <td>2-4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1922</th>\n",
" <td>50th</td>\n",
" <td>4-6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1923</th>\n",
" <td>50th</td>\n",
" <td>6-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1924</th>\n",
" <td>50th</td>\n",
" <td>ABOVE_12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1925 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" AGE_PERCENTIL WINDOW\n",
"0 60th 0-2\n",
"1 60th 2-4\n",
"2 60th 4-6\n",
"3 60th 6-12\n",
"4 60th ABOVE_12\n",
"... ... ...\n",
"1920 50th 0-2\n",
"1921 50th 2-4\n",
"1922 50th 4-6\n",
"1923 50th 6-12\n",
"1924 50th ABOVE_12\n",
"\n",
"[1925 rows x 2 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "os1VJzJnQpjh",
"outputId": "4c5e995a-8016-4b1c-d24e-41b9ee129bf6"
},
"source": [
"without_ICU_column = data.drop('ICU', axis = 1) #seperating the ICU lable column\n",
"ICU_column = data['ICU']\n",
"colums_to_convert = data.select_dtypes(object).columns #finding columns that are not of type float or int\n",
"colums_to_convert"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['AGE_PERCENTIL', 'WINDOW'], dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 249
},
"id": "bCGGUrAiRHbq",
"outputId": "5ab0d36b-d58b-408e-f6ad-963d6303abfe"
},
"source": [
"without_ICU_column = pd.get_dummies(without_ICU_column, columns = colums_to_convert) #performing hotcoding\n",
"without_ICU_column.head()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PATIENT_VISIT_IDENTIFIER</th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>GENDER</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>WINDOW_0-2</th>\n",
" <th>WINDOW_2-4</th>\n",
" <th>WINDOW_4-6</th>\n",
" <th>WINDOW_6-12</th>\n",
" <th>WINDOW_ABOVE_12</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.593220</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.586207</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.237113</td>\n",
" <td>0.0000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>0.898990</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.420290</td>\n",
" <td>0.736842</td>\n",
" <td>-1.00000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.107143</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.107143</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.318681</td>\n",
" <td>0.898990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.275362</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-0.871658</td>\n",
" <td>-1.0</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-0.863874</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-0.414634</td>\n",
" <td>-1.0</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-0.979069</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>-1.0</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-0.926398</td>\n",
" <td>-1.0</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-0.859275</td>\n",
" <td>-1.0</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-0.669393</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-0.320755</td>\n",
" <td>-1.0</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-0.353659</td>\n",
" <td>-1.0</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-0.979925</td>\n",
" <td>-1.0</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-0.963023</td>\n",
" <td>-1.0</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-0.762843</td>\n",
" <td>-1.0</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-0.643154</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-0.365854</td>\n",
" <td>-1.0</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.230769</td>\n",
" <td>-1.0</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-0.875862</td>\n",
" <td>-1.0</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-0.815951</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>0.574468</td>\n",
" <td>-1.0</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>0.393939</td>\n",
" <td>-1.0</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
" <td>-1.0</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-1.0</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>0.848485</td>\n",
" <td>-1.0</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
" <td>0.925926</td>\n",
" <td>-1.0</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>-1.0</td>\n",
" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-0.999067</td>\n",
" <td>-1.0</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-0.983994</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-1.0</td>\n",
" <td>-0.243021</td>\n",
" <td>-0.338537</td>\n",
" <td>-0.213031</td>\n",
" <td>-0.317859</td>\n",
" <td>0.033779</td>\n",
" <td>0.665932</td>\n",
" <td>-0.283951</td>\n",
" <td>-0.376923</td>\n",
" <td>-0.188679</td>\n",
" <td>-0.379310</td>\n",
" <td>0.035714</td>\n",
" <td>0.631579</td>\n",
" <td>-0.340206</td>\n",
" <td>-0.4875</td>\n",
" <td>-0.572650</td>\n",
" <td>-0.857143</td>\n",
" <td>0.098901</td>\n",
" <td>0.797980</td>\n",
" <td>-0.076923</td>\n",
" <td>0.286486</td>\n",
" <td>0.298507</td>\n",
" <td>0.272727</td>\n",
" <td>0.362319</td>\n",
" <td>0.947368</td>\n",
" <td>-0.33913</td>\n",
" <td>0.325153</td>\n",
" <td>0.114504</td>\n",
" <td>0.176471</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.818182</td>\n",
" <td>-0.389967</td>\n",
" <td>0.407558</td>\n",
" <td>-0.230462</td>\n",
" <td>0.096774</td>\n",
" <td>-0.242282</td>\n",
" <td>-0.814433</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 GENDER DISEASE GROUPING 1 \\\n",
"0 0 1 0 0.0 \n",
"1 0 1 0 0.0 \n",
"2 0 1 0 0.0 \n",
"3 0 1 0 0.0 \n",
"4 0 1 0 0.0 \n",
"\n",
" DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" DISEASE GROUPING 5 DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"0 1.0 1.0 0.0 0.0 1.0 \n",
"1 1.0 1.0 0.0 0.0 1.0 \n",
"2 1.0 1.0 0.0 0.0 1.0 \n",
"3 1.0 1.0 0.0 0.0 1.0 \n",
"4 1.0 1.0 0.0 0.0 1.0 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.000000 0.000000 0.000000 0.000000 -1.0 \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.871658 -0.871658 -0.871658 -0.871658 \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -1.000000 -1.000000 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.863874 -0.863874 -0.863874 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.0 -0.317073 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.863874 -1.0 -0.317073 -0.317073 \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -1.0 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.317073 -0.317073 -1.0 -0.414634 \n",
"\n",
" BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -0.317073 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.414634 -0.414634 -0.414634 -1.0 \n",
"\n",
" BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.979069 -0.979069 -0.979069 -0.979069 \n",
"\n",
" BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 0.183673 0.183673 0.183673 0.183673 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 0.326531 0.326531 0.326531 0.326531 \n",
"\n",
" CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.868365 -0.868365 -0.868365 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.926398 -0.926398 -0.926398 \n",
"\n",
" CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.868365 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.926398 -1.0 -0.859275 -0.859275 -0.859275 -0.859275 \n",
"\n",
" FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.669393 -0.669393 -0.669393 -0.669393 -1.0 \n",
"\n",
" GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"\n",
" HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.090147 0.090147 0.090147 0.090147 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.320755 -0.320755 -0.320755 -0.320755 \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.109756 0.109756 0.109756 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.353659 -0.353659 -0.353659 \n",
"\n",
" HEMOGLOBIN_MAX HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN INR_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 0.109756 -1.0 -0.932246 -0.932246 -0.932246 -0.932246 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.353659 -1.0 -0.979925 -0.979925 -0.979925 -0.979925 \n",
"\n",
" INR_DIFF LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 1.000000 1.000000 1.000000 1.000000 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.963023 -0.963023 -0.963023 -0.963023 \n",
"\n",
" LACTATE_DIFF LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.835844 -0.835844 -0.835844 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.762843 -0.762843 -0.762843 \n",
"\n",
" LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.835844 -1.0 -0.914938 -0.914938 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.762843 -1.0 -0.643154 -0.643154 \n",
"\n",
" LINFOCITOS_MIN LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.914938 -0.914938 -1.0 -0.868747 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.643154 -0.643154 -1.0 -0.868747 \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN NEUTROPHILES_MAX NEUTROPHILES_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.868747 -0.868747 -0.868747 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.868747 -0.868747 -0.868747 -1.0 \n",
"\n",
" P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN P02_ARTERIAL_MIN P02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.170732 -0.170732 -0.170732 -0.170732 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.365854 -0.365854 -0.365854 -0.365854 \n",
"\n",
" P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.704142 -0.704142 -0.704142 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.230769 -0.230769 -0.230769 \n",
"\n",
" P02_VENOUS_MAX P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.704142 -1.0 -0.779310 -0.779310 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.230769 -1.0 -0.875862 -0.875862 \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.779310 -0.779310 -1.0 \n",
"3 NaN NaN NaN \n",
"4 -0.875862 -0.875862 -1.0 \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.815951 -0.815951 -0.815951 -0.815951 \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.875236 -0.875236 -0.875236 -0.875236 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"\n",
" PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN PH_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.234043 0.234043 0.234043 0.234043 \n",
"3 NaN NaN NaN NaN \n",
"4 0.574468 0.574468 0.574468 0.574468 \n",
"\n",
" PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.363636 0.363636 0.363636 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 0.393939 0.393939 0.393939 \n",
"\n",
" PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN PLATELETS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.363636 -1.0 -0.540721 -0.540721 \n",
"3 NaN NaN NaN NaN \n",
"4 0.393939 -1.0 -0.471295 -0.471295 \n",
"\n",
" PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF POTASSIUM_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.540721 -0.540721 -1.0 -0.518519 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.471295 -0.471295 -1.0 -0.666667 \n",
"\n",
" POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.518519 -0.518519 -0.518519 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.666667 -0.666667 -0.666667 -1.0 \n",
"\n",
" SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN SAT02_ARTERIAL_MIN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 0.939394 0.939394 \n",
"3 NaN NaN NaN \n",
"4 0.848485 0.848485 0.848485 \n",
"\n",
" SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 -1.0 0.345679 \n",
"3 NaN NaN NaN \n",
"4 0.848485 -1.0 0.925926 \n",
"\n",
" SAT02_VENOUS_MEAN SAT02_VENOUS_MIN SAT02_VENOUS_MAX SAT02_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.345679 0.345679 0.345679 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 0.925926 0.925926 0.925926 -1.0 \n",
"\n",
" SODIUM_MEDIAN SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"\n",
" TGO_MEDIAN TGO_MEAN TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.997201 -0.997201 -0.997201 -0.997201 -1.0 -0.990854 -0.990854 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.999067 -0.999067 -0.999067 -0.999067 -1.0 -0.983994 -0.983994 \n",
"\n",
" TGP_MIN TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.990854 -0.990854 -1.0 -0.825613 -0.825613 -0.825613 -0.825613 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.983994 -0.983994 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.994912 -0.994912 -0.994912 -0.994912 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.996762 -0.996762 -0.996762 -0.996762 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN HEART_RATE_MEAN \\\n",
"0 0.086420 -0.230769 -0.283019 \n",
"1 0.333333 -0.230769 -0.132075 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.243021 -0.338537 -0.213031 \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"0 -0.593220 -0.285714 0.736842 \n",
"1 -0.593220 0.535714 0.578947 \n",
"2 NaN NaN NaN \n",
"3 NaN -0.107143 0.736842 \n",
"4 -0.317859 0.033779 0.665932 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEDIAN BLOODPRESSURE_SISTOLIC_MEDIAN \\\n",
"0 0.086420 -0.230769 \n",
"1 0.333333 -0.230769 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 -0.283951 -0.376923 \n",
"\n",
" HEART_RATE_MEDIAN RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN \\\n",
"0 -0.283019 -0.586207 -0.285714 \n",
"1 -0.132075 -0.586207 0.535714 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN -0.107143 \n",
"4 -0.188679 -0.379310 0.035714 \n",
"\n",
" OXYGEN_SATURATION_MEDIAN BLOODPRESSURE_DIASTOLIC_MIN \\\n",
"0 0.736842 0.237113 \n",
"1 0.578947 0.443299 \n",
"2 NaN NaN \n",
"3 0.736842 NaN \n",
"4 0.631579 -0.340206 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.0000 -0.162393 -0.500000 \n",
"1 0.0000 -0.025641 -0.500000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.4875 -0.572650 -0.857143 \n",
"\n",
" TEMPERATURE_MIN OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"0 0.208791 0.898990 -0.247863 \n",
"1 0.714286 0.838384 -0.076923 \n",
"2 NaN NaN NaN \n",
"3 0.318681 0.898990 NaN \n",
"4 0.098901 0.797980 -0.076923 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"0 -0.459459 -0.432836 -0.636364 \n",
"1 -0.459459 -0.313433 -0.636364 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 0.286486 0.298507 0.272727 \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.420290 0.736842 -1.00000 \n",
"1 0.246377 0.578947 -1.00000 \n",
"2 NaN NaN NaN \n",
"3 -0.275362 0.736842 NaN \n",
"4 0.362319 0.947368 -0.33913 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 0.325153 0.114504 0.176471 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 -1.000000 -1.000000 NaN \n",
"4 -0.238095 -0.818182 -0.389967 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 0.407558 -0.230462 \n",
"\n",
" RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN -1.000000 \n",
"4 0.096774 -0.242282 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"0 -1.000000 0 0 \n",
"1 -1.000000 0 0 \n",
"2 NaN 0 0 \n",
"3 -1.000000 0 0 \n",
"4 -0.814433 0 0 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"2 1 0 0 \n",
"3 1 0 0 \n",
"4 1 0 0 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th WINDOW_0-2 WINDOW_2-4 \\\n",
"0 0 0 1 0 \n",
"1 0 0 0 1 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" WINDOW_4-6 WINDOW_6-12 WINDOW_ABOVE_12 \n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 1 0 0 \n",
"3 0 1 0 \n",
"4 0 0 1 "
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 249
},
"id": "NqK7eIDgTn4r",
"outputId": "4565e34c-7fdd-463b-8abe-6286231709f1"
},
"source": [
"data_expand = pd.concat([without_ICU_column, ICU_column], axis = 1) #adding the ICU column again at the last position\n",
"data_expand.head(5)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PATIENT_VISIT_IDENTIFIER</th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>GENDER</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>WINDOW_0-2</th>\n",
" <th>WINDOW_2-4</th>\n",
" <th>WINDOW_4-6</th>\n",
" <th>WINDOW_6-12</th>\n",
" <th>WINDOW_ABOVE_12</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>-0.983994</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-0.996762</td>\n",
" <td>-1.0</td>\n",
" <td>-0.243021</td>\n",
" <td>-0.338537</td>\n",
" <td>-0.213031</td>\n",
" <td>-0.317859</td>\n",
" <td>0.033779</td>\n",
" <td>0.665932</td>\n",
" <td>-0.283951</td>\n",
" <td>-0.376923</td>\n",
" <td>-0.188679</td>\n",
" <td>-0.379310</td>\n",
" <td>0.035714</td>\n",
" <td>0.631579</td>\n",
" <td>-0.340206</td>\n",
" <td>-0.4875</td>\n",
" <td>-0.572650</td>\n",
" <td>-0.857143</td>\n",
" <td>0.098901</td>\n",
" <td>0.797980</td>\n",
" <td>-0.076923</td>\n",
" <td>0.286486</td>\n",
" <td>0.298507</td>\n",
" <td>0.272727</td>\n",
" <td>0.362319</td>\n",
" <td>0.947368</td>\n",
" <td>-0.33913</td>\n",
" <td>0.325153</td>\n",
" <td>0.114504</td>\n",
" <td>0.176471</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.818182</td>\n",
" <td>-0.389967</td>\n",
" <td>0.407558</td>\n",
" <td>-0.230462</td>\n",
" <td>0.096774</td>\n",
" <td>-0.242282</td>\n",
" <td>-0.814433</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 GENDER DISEASE GROUPING 1 \\\n",
"0 0 1 0 0.0 \n",
"1 0 1 0 0.0 \n",
"2 0 1 0 0.0 \n",
"3 0 1 0 0.0 \n",
"4 0 1 0 0.0 \n",
"\n",
" DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" DISEASE GROUPING 5 DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"0 1.0 1.0 0.0 0.0 1.0 \n",
"1 1.0 1.0 0.0 0.0 1.0 \n",
"2 1.0 1.0 0.0 0.0 1.0 \n",
"3 1.0 1.0 0.0 0.0 1.0 \n",
"4 1.0 1.0 0.0 0.0 1.0 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.000000 0.000000 0.000000 0.000000 -1.0 \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.871658 -0.871658 -0.871658 -0.871658 \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -1.000000 -1.000000 -1.000000 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.863874 -0.863874 -0.863874 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.000000 -1.0 -0.317073 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.863874 -1.0 -0.317073 -0.317073 \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -1.0 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.317073 -0.317073 -1.0 -0.414634 \n",
"\n",
" BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -0.317073 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.414634 -0.414634 -0.414634 -1.0 \n",
"\n",
" BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.979069 -0.979069 -0.979069 -0.979069 \n",
"\n",
" BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 0.183673 0.183673 0.183673 0.183673 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 0.326531 0.326531 0.326531 0.326531 \n",
"\n",
" CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.868365 -0.868365 -0.868365 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.926398 -0.926398 -0.926398 \n",
"\n",
" CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.868365 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.926398 -1.0 -0.859275 -0.859275 -0.859275 -0.859275 \n",
"\n",
" FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.669393 -0.669393 -0.669393 -0.669393 -1.0 \n",
"\n",
" GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"\n",
" HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.090147 0.090147 0.090147 0.090147 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.320755 -0.320755 -0.320755 -0.320755 \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.109756 0.109756 0.109756 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.353659 -0.353659 -0.353659 \n",
"\n",
" HEMOGLOBIN_MAX HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN INR_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 0.109756 -1.0 -0.932246 -0.932246 -0.932246 -0.932246 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.353659 -1.0 -0.979925 -0.979925 -0.979925 -0.979925 \n",
"\n",
" INR_DIFF LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 1.000000 1.000000 1.000000 1.000000 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.963023 -0.963023 -0.963023 -0.963023 \n",
"\n",
" LACTATE_DIFF LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.835844 -0.835844 -0.835844 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.762843 -0.762843 -0.762843 \n",
"\n",
" LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.835844 -1.0 -0.914938 -0.914938 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.762843 -1.0 -0.643154 -0.643154 \n",
"\n",
" LINFOCITOS_MIN LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.914938 -0.914938 -1.0 -0.868747 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.643154 -0.643154 -1.0 -0.868747 \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN NEUTROPHILES_MAX NEUTROPHILES_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.868747 -0.868747 -0.868747 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.868747 -0.868747 -0.868747 -1.0 \n",
"\n",
" P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN P02_ARTERIAL_MIN P02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.170732 -0.170732 -0.170732 -0.170732 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.365854 -0.365854 -0.365854 -0.365854 \n",
"\n",
" P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.704142 -0.704142 -0.704142 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.230769 -0.230769 -0.230769 \n",
"\n",
" P02_VENOUS_MAX P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.704142 -1.0 -0.779310 -0.779310 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.230769 -1.0 -0.875862 -0.875862 \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.779310 -0.779310 -1.0 \n",
"3 NaN NaN NaN \n",
"4 -0.875862 -0.875862 -1.0 \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.815951 -0.815951 -0.815951 -0.815951 \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.875236 -0.875236 -0.875236 -0.875236 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"\n",
" PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN PH_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.234043 0.234043 0.234043 0.234043 \n",
"3 NaN NaN NaN NaN \n",
"4 0.574468 0.574468 0.574468 0.574468 \n",
"\n",
" PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.363636 0.363636 0.363636 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 0.393939 0.393939 0.393939 \n",
"\n",
" PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN PLATELETS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.363636 -1.0 -0.540721 -0.540721 \n",
"3 NaN NaN NaN NaN \n",
"4 0.393939 -1.0 -0.471295 -0.471295 \n",
"\n",
" PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF POTASSIUM_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.540721 -0.540721 -1.0 -0.518519 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.471295 -0.471295 -1.0 -0.666667 \n",
"\n",
" POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.518519 -0.518519 -0.518519 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.666667 -0.666667 -0.666667 -1.0 \n",
"\n",
" SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN SAT02_ARTERIAL_MIN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 0.939394 0.939394 \n",
"3 NaN NaN NaN \n",
"4 0.848485 0.848485 0.848485 \n",
"\n",
" SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 -1.0 0.345679 \n",
"3 NaN NaN NaN \n",
"4 0.848485 -1.0 0.925926 \n",
"\n",
" SAT02_VENOUS_MEAN SAT02_VENOUS_MIN SAT02_VENOUS_MAX SAT02_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.345679 0.345679 0.345679 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 0.925926 0.925926 0.925926 -1.0 \n",
"\n",
" SODIUM_MEDIAN SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"\n",
" TGO_MEDIAN TGO_MEAN TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.997201 -0.997201 -0.997201 -0.997201 -1.0 -0.990854 -0.990854 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.999067 -0.999067 -0.999067 -0.999067 -1.0 -0.983994 -0.983994 \n",
"\n",
" TGP_MIN TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.990854 -0.990854 -1.0 -0.825613 -0.825613 -0.825613 -0.825613 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.983994 -0.983994 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.994912 -0.994912 -0.994912 -0.994912 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.996762 -0.996762 -0.996762 -0.996762 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN HEART_RATE_MEAN \\\n",
"0 0.086420 -0.230769 -0.283019 \n",
"1 0.333333 -0.230769 -0.132075 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.243021 -0.338537 -0.213031 \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"0 -0.593220 -0.285714 0.736842 \n",
"1 -0.593220 0.535714 0.578947 \n",
"2 NaN NaN NaN \n",
"3 NaN -0.107143 0.736842 \n",
"4 -0.317859 0.033779 0.665932 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEDIAN BLOODPRESSURE_SISTOLIC_MEDIAN \\\n",
"0 0.086420 -0.230769 \n",
"1 0.333333 -0.230769 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 -0.283951 -0.376923 \n",
"\n",
" HEART_RATE_MEDIAN RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN \\\n",
"0 -0.283019 -0.586207 -0.285714 \n",
"1 -0.132075 -0.586207 0.535714 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN -0.107143 \n",
"4 -0.188679 -0.379310 0.035714 \n",
"\n",
" OXYGEN_SATURATION_MEDIAN BLOODPRESSURE_DIASTOLIC_MIN \\\n",
"0 0.736842 0.237113 \n",
"1 0.578947 0.443299 \n",
"2 NaN NaN \n",
"3 0.736842 NaN \n",
"4 0.631579 -0.340206 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.0000 -0.162393 -0.500000 \n",
"1 0.0000 -0.025641 -0.500000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 -0.4875 -0.572650 -0.857143 \n",
"\n",
" TEMPERATURE_MIN OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"0 0.208791 0.898990 -0.247863 \n",
"1 0.714286 0.838384 -0.076923 \n",
"2 NaN NaN NaN \n",
"3 0.318681 0.898990 NaN \n",
"4 0.098901 0.797980 -0.076923 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"0 -0.459459 -0.432836 -0.636364 \n",
"1 -0.459459 -0.313433 -0.636364 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 0.286486 0.298507 0.272727 \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.420290 0.736842 -1.00000 \n",
"1 0.246377 0.578947 -1.00000 \n",
"2 NaN NaN NaN \n",
"3 -0.275362 0.736842 NaN \n",
"4 0.362319 0.947368 -0.33913 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 0.325153 0.114504 0.176471 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 -1.000000 -1.000000 NaN \n",
"4 -0.238095 -0.818182 -0.389967 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 0.407558 -0.230462 \n",
"\n",
" RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN -1.000000 \n",
"4 0.096774 -0.242282 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"0 -1.000000 0 0 \n",
"1 -1.000000 0 0 \n",
"2 NaN 0 0 \n",
"3 -1.000000 0 0 \n",
"4 -0.814433 0 0 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"2 1 0 0 \n",
"3 1 0 0 \n",
"4 1 0 0 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th WINDOW_0-2 WINDOW_2-4 \\\n",
"0 0 0 1 0 \n",
"1 0 0 0 1 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" WINDOW_4-6 WINDOW_6-12 WINDOW_ABOVE_12 ICU \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 1 0 0 0 \n",
"3 0 1 0 0 \n",
"4 0 0 1 1 "
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 531
},
"id": "h6cspkGYpYr1",
"outputId": "2e97ae74-f193-4785-d197-93918f7721e7"
},
"source": [
"column_names = data_expand.columns\n",
"arr = data_expand.to_numpy()\n",
"print(arr)\n",
"i=0\n",
"ICU_admitted_rows = []\n",
"while(i<len(arr)): #loop to record the rows in which patient is admitted to the ICU and adding 1 label to the previous rows.\n",
" for j in range(5):\n",
" if(arr[i+j][-1]==1):\n",
" for k in range(j):\n",
" arr[i+k][-1]=1\n",
" for toremove in range(i+j,i+5):\n",
" ICU_admitted_rows.append(toremove)\n",
" break\n",
" i+=5\n",
"print(ICU_admitted_rows)\n",
"deletedcount = 0\n",
"for rowToRemove in ICU_admitted_rows: #removing the rows in which patient was admitted to the ICU\n",
" arr = np.delete(arr, rowToRemove-deletedcount, axis=0)\n",
" deletedcount+=1\n",
"df = pd.DataFrame(arr, columns = column_names)\n",
"df.head(10)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
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" [384. 0. 1. ... 0. 0. 0.]\n",
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],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PATIENT_VISIT_IDENTIFIER</th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>GENDER</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
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" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
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" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
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" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
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" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
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" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>WINDOW_0-2</th>\n",
" <th>WINDOW_2-4</th>\n",
" <th>WINDOW_4-6</th>\n",
" <th>WINDOW_6-12</th>\n",
" <th>WINDOW_ABOVE_12</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>-1.0</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.107143</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.107143</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.318681</td>\n",
" <td>0.898990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.275362</td>\n",
" <td>0.736842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.93895</td>\n",
" <td>-0.93895</td>\n",
" <td>-0.93895</td>\n",
" <td>-0.93895</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-1.0</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-1.0</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>-1.0</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-1.0</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-1.0</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>2.0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>-0.762162</td>\n",
" <td>0.000000</td>\n",
" <td>-0.696970</td>\n",
" <td>0.101449</td>\n",
" <td>1.000000</td>\n",
" <td>-0.704348</td>\n",
" <td>-0.693252</td>\n",
" <td>-0.541985</td>\n",
" <td>-0.941176</td>\n",
" <td>-1.0</td>\n",
" <td>-0.797980</td>\n",
" <td>-0.658863</td>\n",
" <td>-0.563758</td>\n",
" <td>-0.721834</td>\n",
" <td>-0.926882</td>\n",
" <td>-1.0</td>\n",
" <td>-0.801293</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 GENDER DISEASE GROUPING 1 \\\n",
"0 0.0 1.0 0.0 0.0 \n",
"1 0.0 1.0 0.0 0.0 \n",
"2 0.0 1.0 0.0 0.0 \n",
"3 0.0 1.0 0.0 0.0 \n",
"4 2.0 0.0 0.0 0.0 \n",
"5 2.0 0.0 0.0 0.0 \n",
"6 2.0 0.0 0.0 0.0 \n",
"7 2.0 0.0 0.0 0.0 \n",
"8 3.0 0.0 1.0 0.0 \n",
"9 3.0 0.0 1.0 0.0 \n",
"\n",
" DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 \n",
"\n",
" DISEASE GROUPING 5 DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"0 1.0 1.0 0.0 0.0 1.0 \n",
"1 1.0 1.0 0.0 0.0 1.0 \n",
"2 1.0 1.0 0.0 0.0 1.0 \n",
"3 1.0 1.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 1.0 \n",
"5 0.0 0.0 0.0 0.0 1.0 \n",
"6 0.0 0.0 0.0 0.0 1.0 \n",
"7 0.0 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 1.0 1.0 \n",
"9 0.0 0.0 0.0 1.0 1.0 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -0.317073 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -0.317073 -0.317073 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -1.0 -0.317073 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.317073 -0.317073 -1.0 -0.317073 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.317073 -0.317073 -0.317073 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.317073 -0.317073 -0.317073 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.93895 -0.93895 -0.93895 -0.93895 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.93895 -0.93895 -0.93895 -0.93895 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 0.183673 0.183673 0.183673 0.183673 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.868365 -0.868365 -0.868365 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.912243 -0.912243 -0.912243 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -0.868365 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -0.912243 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"5 NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN \n",
"\n",
" FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"5 NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN \n",
"\n",
" GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.780261 -0.780261 -0.780261 -0.780261 -1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.090147 0.090147 0.090147 0.090147 \n",
"3 NaN NaN NaN NaN \n",
"4 0.144654 0.144654 0.144654 0.144654 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.109756 0.109756 0.109756 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 0.158537 0.158537 0.158537 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" HEMOGLOBIN_MAX HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN INR_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 0.109756 -1.0 -0.932246 -0.932246 -0.932246 -0.932246 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 0.158537 -1.0 -0.959849 -0.959849 -0.959849 -0.959849 \n",
"5 NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN \n",
"\n",
" INR_DIFF LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -1.0 1.0 1.0 1.0 1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -1.0 1.0 1.0 1.0 1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" LACTATE_DIFF LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.835844 -0.835844 -0.835844 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.382773 -0.382773 -0.382773 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.835844 -1.0 -0.914938 -0.914938 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.382773 -1.0 -0.908714 -0.908714 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" LINFOCITOS_MIN LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.914938 -0.914938 -1.0 -0.868747 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.908714 -0.908714 -1.0 -0.412965 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN NEUTROPHILES_MAX NEUTROPHILES_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.868747 -0.868747 -0.868747 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.412965 -0.412965 -0.412965 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN P02_ARTERIAL_MIN P02_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.170732 -0.170732 -0.170732 -0.170732 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.170732 -0.170732 -0.170732 -0.170732 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 -0.704142 -0.704142 -0.704142 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 -0.704142 -0.704142 -0.704142 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" P02_VENOUS_MAX P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.704142 -1.0 -0.77931 -0.77931 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.704142 -1.0 -0.77931 -0.77931 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 -0.77931 -0.77931 -1.0 \n",
"3 NaN NaN NaN \n",
"4 -0.77931 -0.77931 -1.0 \n",
"5 NaN NaN NaN \n",
"6 NaN NaN NaN \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.875236 -0.875236 -0.875236 -0.875236 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.939887 -0.939887 -0.939887 -0.939887 -1.0 \n",
"5 NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN \n",
"\n",
" PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN PH_ARTERIAL_MAX \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.234043 0.234043 0.234043 0.234043 \n",
"3 NaN NaN NaN NaN \n",
"4 0.234043 0.234043 0.234043 0.234043 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -1.0 0.363636 0.363636 0.363636 \n",
"3 NaN NaN NaN NaN \n",
"4 -1.0 0.363636 0.363636 0.363636 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN PLATELETS_MEAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.363636 -1.0 -0.540721 -0.540721 \n",
"3 NaN NaN NaN NaN \n",
"4 0.363636 -1.0 -0.399199 -0.399199 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF POTASSIUM_MEDIAN \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.540721 -0.540721 -1.0 -0.518519 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.399199 -0.399199 -1.0 -0.703704 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 -0.518519 -0.518519 -0.518519 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 -0.703704 -0.703704 -0.703704 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN SAT02_ARTERIAL_MIN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 0.939394 0.939394 \n",
"3 NaN NaN NaN \n",
"4 0.939394 0.939394 0.939394 \n",
"5 NaN NaN NaN \n",
"6 NaN NaN NaN \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 0.939394 -1.0 0.345679 \n",
"3 NaN NaN NaN \n",
"4 0.939394 -1.0 0.345679 \n",
"5 NaN NaN NaN \n",
"6 NaN NaN NaN \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" SAT02_VENOUS_MEAN SAT02_VENOUS_MIN SAT02_VENOUS_MAX SAT02_VENOUS_DIFF \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 0.345679 0.345679 0.345679 -1.0 \n",
"3 NaN NaN NaN NaN \n",
"4 0.345679 0.345679 0.345679 -1.0 \n",
"5 NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN \n",
"\n",
" SODIUM_MEDIAN SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 0.085714 0.085714 0.085714 0.085714 -1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" TGO_MEDIAN TGO_MEAN TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.997201 -0.997201 -0.997201 -0.997201 -1.0 -0.990854 -0.990854 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"5 NaN NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" TGP_MIN TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 -0.990854 -0.990854 -1.0 -0.825613 -0.825613 -0.825613 -0.825613 \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"5 NaN NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"5 NaN NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN NaN \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 -0.994912 -0.994912 -0.994912 -0.994912 -1.0 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN NaN NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN HEART_RATE_MEAN \\\n",
"0 0.086420 -0.230769 -0.283019 \n",
"1 0.333333 -0.230769 -0.132075 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.489712 -0.685470 -0.048218 \n",
"7 -0.612654 -0.828846 0.037736 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"0 -0.593220 -0.285714 0.736842 \n",
"1 -0.593220 0.535714 0.578947 \n",
"2 NaN NaN NaN \n",
"3 NaN -0.107143 0.736842 \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.645951 NaN 0.935673 \n",
"7 -0.720339 0.357143 0.799342 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEDIAN BLOODPRESSURE_SISTOLIC_MEDIAN \\\n",
"0 0.086420 -0.230769 \n",
"1 0.333333 -0.230769 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 -0.506173 -0.815385 \n",
"7 -0.604938 -0.846154 \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"\n",
" HEART_RATE_MEDIAN RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN \\\n",
"0 -0.283019 -0.586207 -0.285714 \n",
"1 -0.132075 -0.586207 0.535714 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN -0.107143 \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.056604 -0.517241 NaN \n",
"7 0.028302 -0.724138 0.357143 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" OXYGEN_SATURATION_MEDIAN BLOODPRESSURE_DIASTOLIC_MIN \\\n",
"0 0.736842 0.237113 \n",
"1 0.578947 0.443299 \n",
"2 NaN NaN \n",
"3 0.736842 NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 0.947368 -0.525773 \n",
"7 0.815789 -0.505155 \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.0000 -0.162393 -0.500000 \n",
"1 0.0000 -0.025641 -0.500000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.5125 -0.111111 -0.714286 \n",
"7 -0.6625 -0.179487 -0.642857 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" TEMPERATURE_MIN OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"0 0.208791 0.898990 -0.247863 \n",
"1 0.714286 0.838384 -0.076923 \n",
"2 NaN NaN NaN \n",
"3 0.318681 0.898990 NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 NaN 0.959596 -0.435897 \n",
"7 0.604396 0.797980 -0.572650 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"0 -0.459459 -0.432836 -0.636364 \n",
"1 -0.459459 -0.313433 -0.636364 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.491892 0.000000 -0.575758 \n",
"7 -0.762162 0.000000 -0.696970 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.420290 0.736842 -1.000000 \n",
"1 0.246377 0.578947 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 -0.275362 0.736842 NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 NaN 1.000000 -0.547826 \n",
"7 0.101449 1.000000 -0.704348 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 -0.533742 -0.603053 -0.764706 \n",
"7 -0.693252 -0.541985 -0.941176 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"0 -1.0 -1.000000 -1.000000 \n",
"1 -1.0 -1.000000 -1.000000 \n",
"2 NaN NaN NaN \n",
"3 -1.0 -1.000000 NaN \n",
"4 NaN NaN NaN \n",
"5 NaN NaN NaN \n",
"6 NaN -0.959596 -0.515528 \n",
"7 -1.0 -0.797980 -0.658863 \n",
"8 NaN NaN NaN \n",
"9 NaN NaN NaN \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"1 -1.000000 -1.000000 \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 -0.351328 -0.747001 \n",
"7 -0.563758 -0.721834 \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"\n",
" RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.0 \n",
"1 -1.000000 -1.0 \n",
"2 NaN NaN \n",
"3 NaN -1.0 \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 -0.756272 NaN \n",
"7 -0.926882 -1.0 \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"0 -1.000000 0.0 0.0 \n",
"1 -1.000000 0.0 0.0 \n",
"2 NaN 0.0 0.0 \n",
"3 -1.000000 0.0 0.0 \n",
"4 NaN 1.0 0.0 \n",
"5 NaN 1.0 0.0 \n",
"6 -0.961262 1.0 0.0 \n",
"7 -0.801293 1.0 0.0 \n",
"8 NaN 0.0 0.0 \n",
"9 NaN 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 1.0 0.0 \n",
"9 0.0 1.0 0.0 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"0 1.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 \n",
"2 1.0 0.0 0.0 \n",
"3 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th WINDOW_0-2 WINDOW_2-4 \\\n",
"0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 1.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 1.0 0.0 \n",
"5 0.0 0.0 0.0 1.0 \n",
"6 0.0 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 0.0 \n",
"8 0.0 0.0 1.0 0.0 \n",
"9 0.0 0.0 0.0 1.0 \n",
"\n",
" WINDOW_4-6 WINDOW_6-12 WINDOW_ABOVE_12 ICU \n",
"0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 1.0 \n",
"2 1.0 0.0 0.0 1.0 \n",
"3 0.0 1.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 1.0 \n",
"5 0.0 0.0 0.0 1.0 \n",
"6 1.0 0.0 0.0 1.0 \n",
"7 0.0 1.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 0.0 "
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 989
},
"id": "ubeiQml1zNfi",
"outputId": "6a7333e2-6a85-4e17-d0dc-9aa302e39a6d"
},
"source": [
"#Filling missing values\n",
"pd.options.mode.chained_assignment = None \n",
"edited_dfs_list = []\n",
"max_patient_id = df['PATIENT_VISIT_IDENTIFIER'].max()\n",
"for i in range(int(max_patient_id)): #keeping only the first window that is 0-2 for every patient and filling NaN values with mean of all windows\n",
" tempdf = df[df['PATIENT_VISIT_IDENTIFIER']==i]\n",
" if(len(tempdf)!=0):\n",
" tempdf.fillna(tempdf.mean(), inplace=True)\n",
" tempdf = tempdf.iloc[[0]]\n",
" edited_dfs_list.append(tempdf)\n",
"\n",
" \n",
"final_data = pd.concat(edited_dfs_list)\n",
"final_data.head(30)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PATIENT_VISIT_IDENTIFIER</th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>GENDER</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>WINDOW_0-2</th>\n",
" <th>WINDOW_2-4</th>\n",
" <th>WINDOW_4-6</th>\n",
" <th>WINDOW_6-12</th>\n",
" <th>WINDOW_ABOVE_12</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
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" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
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" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
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" <td>-0.868365</td>\n",
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" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
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" <td>-0.891993</td>\n",
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" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>-1.0</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>-1.0</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-1.0</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-1.0</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-1.0</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-1.0</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
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" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
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" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-1.0</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.593220</td>\n",
" <td>-2.857143e-01</td>\n",
" <td>0.736842</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.586207</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.237113</td>\n",
" <td>0.000000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>0.898990</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.420290</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.0</td>\n",
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" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-1.0</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-1.0</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>-1.0</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-1.0</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
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" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
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" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
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" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
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" <td>-0.754601</td>\n",
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" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
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" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
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" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-1.0</td>\n",
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" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
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" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
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" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-1.0</td>\n",
" <td>-0.551183</td>\n",
" <td>-0.757158</td>\n",
" <td>-0.005241</td>\n",
" <td>-0.683145</td>\n",
" <td>3.571429e-01</td>\n",
" <td>0.867507</td>\n",
" <td>-0.555556</td>\n",
" <td>-0.830769</td>\n",
" <td>-0.014151</td>\n",
" <td>-0.620690</td>\n",
" <td>0.357143</td>\n",
" <td>0.881579</td>\n",
" <td>-0.515464</td>\n",
" <td>-0.587500</td>\n",
" <td>-0.145299</td>\n",
" <td>-0.678571</td>\n",
" <td>0.604396</td>\n",
" <td>0.878788</td>\n",
" <td>-0.504274</td>\n",
" <td>-0.627027</td>\n",
" <td>0.000000</td>\n",
" <td>-0.636364</td>\n",
" <td>0.101449</td>\n",
" <td>1.000000</td>\n",
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" <td>-0.852941</td>\n",
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" <td>-0.878788</td>\n",
" <td>-0.587195</td>\n",
" <td>-0.457543</td>\n",
" <td>-0.734417</td>\n",
" <td>-0.841577</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.881277</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.263158</td>\n",
" <td>-0.263158</td>\n",
" <td>-0.263158</td>\n",
" <td>-0.263158</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.972789</td>\n",
" <td>-0.972789</td>\n",
" <td>-0.972789</td>\n",
" <td>-0.972789</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>0.326531</td>\n",
" <td>-1.0</td>\n",
" <td>-0.968861</td>\n",
" <td>-0.968861</td>\n",
" <td>-0.968861</td>\n",
" <td>-0.968861</td>\n",
" <td>-1.0</td>\n",
" <td>-0.194030</td>\n",
" <td>-0.194030</td>\n",
" <td>-0.194030</td>\n",
" <td>-0.194030</td>\n",
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" <td>-0.316589</td>\n",
" <td>-0.316589</td>\n",
" <td>-0.316589</td>\n",
" <td>-0.316589</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
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" <td>-0.219512</td>\n",
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" <td>-0.219512</td>\n",
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" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
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" <td>-0.828421</td>\n",
" <td>-0.828421</td>\n",
" <td>-0.828421</td>\n",
" <td>-0.828421</td>\n",
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" <td>-0.836100</td>\n",
" <td>-0.836100</td>\n",
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" <td>-0.779141</td>\n",
" <td>-0.779141</td>\n",
" <td>-0.779141</td>\n",
" <td>-0.779141</td>\n",
" <td>-1.0</td>\n",
" <td>-0.503592</td>\n",
" <td>-0.503592</td>\n",
" <td>-0.503592</td>\n",
" <td>-0.503592</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.564753</td>\n",
" <td>-0.564753</td>\n",
" <td>-0.564753</td>\n",
" <td>-0.564753</td>\n",
" <td>-1.0</td>\n",
" <td>-0.777778</td>\n",
" <td>-0.777778</td>\n",
" <td>-0.777778</td>\n",
" <td>-0.777778</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.580247</td>\n",
" <td>0.580247</td>\n",
" <td>0.580247</td>\n",
" <td>0.580247</td>\n",
" <td>-1.0</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.989549</td>\n",
" <td>-0.989549</td>\n",
" <td>-0.989549</td>\n",
" <td>-0.989549</td>\n",
" <td>-1.0</td>\n",
" <td>-0.956555</td>\n",
" <td>-0.956555</td>\n",
" <td>-0.956555</td>\n",
" <td>-0.956555</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.937349</td>\n",
" <td>-0.937349</td>\n",
" <td>-0.937349</td>\n",
" <td>-0.937349</td>\n",
" <td>-1.0</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-1.0</td>\n",
" <td>-0.132620</td>\n",
" <td>-0.484650</td>\n",
" <td>-0.448553</td>\n",
" <td>-0.506215</td>\n",
" <td>-1.197619e-01</td>\n",
" <td>0.652398</td>\n",
" <td>-0.102881</td>\n",
" <td>-0.482051</td>\n",
" <td>-0.459119</td>\n",
" <td>-0.494253</td>\n",
" <td>-0.142857</td>\n",
" <td>0.666667</td>\n",
" <td>-0.127148</td>\n",
" <td>-0.329167</td>\n",
" <td>-0.435897</td>\n",
" <td>-0.547619</td>\n",
" <td>0.216117</td>\n",
" <td>0.629630</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.506306</td>\n",
" <td>-0.263682</td>\n",
" <td>-0.454545</td>\n",
" <td>-0.024155</td>\n",
" <td>0.754386</td>\n",
" <td>-0.692754</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.582697</td>\n",
" <td>-0.784314</td>\n",
" <td>-0.682540</td>\n",
" <td>-0.723906</td>\n",
" <td>-0.769565</td>\n",
" <td>-0.685906</td>\n",
" <td>-0.689698</td>\n",
" <td>-0.776583</td>\n",
" <td>-0.682540</td>\n",
" <td>-0.724145</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.935113</td>\n",
" <td>-0.935113</td>\n",
" <td>-0.935113</td>\n",
" <td>-0.935113</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.913659</td>\n",
" <td>-0.913659</td>\n",
" <td>-0.913659</td>\n",
" <td>-0.913659</td>\n",
" <td>-1.0</td>\n",
" <td>-0.829424</td>\n",
" <td>-0.829424</td>\n",
" <td>-0.829424</td>\n",
" <td>-0.829424</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938084</td>\n",
" <td>-0.938084</td>\n",
" <td>-0.938084</td>\n",
" <td>-0.938084</td>\n",
" <td>-1.0</td>\n",
" <td>-0.851024</td>\n",
" <td>-0.851024</td>\n",
" <td>-0.851024</td>\n",
" <td>-0.851024</td>\n",
" <td>-1.0</td>\n",
" <td>0.358491</td>\n",
" <td>0.358491</td>\n",
" <td>0.358491</td>\n",
" <td>0.358491</td>\n",
" <td>-1.0</td>\n",
" <td>0.304878</td>\n",
" <td>0.304878</td>\n",
" <td>0.304878</td>\n",
" <td>0.304878</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>-1.0</td>\n",
" <td>-0.702202</td>\n",
" <td>-0.702202</td>\n",
" <td>-0.702202</td>\n",
" <td>-0.702202</td>\n",
" <td>-1.0</td>\n",
" <td>-0.641079</td>\n",
" <td>-0.641079</td>\n",
" <td>-0.641079</td>\n",
" <td>-0.641079</td>\n",
" <td>-1.0</td>\n",
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" <td>-0.812725</td>\n",
" <td>-0.812725</td>\n",
" <td>-0.812725</td>\n",
" <td>-1.0</td>\n",
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" <td>-0.170732</td>\n",
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" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
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" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
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" <td>-0.754601</td>\n",
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" <td>-0.754601</td>\n",
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" <td>-0.990926</td>\n",
" <td>-0.990926</td>\n",
" <td>-0.990926</td>\n",
" <td>-0.990926</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
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" <td>-0.457944</td>\n",
" <td>-0.457944</td>\n",
" <td>-0.457944</td>\n",
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" <td>-0.592593</td>\n",
" <td>-0.592593</td>\n",
" <td>-0.592593</td>\n",
" <td>-1.0</td>\n",
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" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>-1.0</td>\n",
" <td>-0.998507</td>\n",
" <td>-0.998507</td>\n",
" <td>-0.998507</td>\n",
" <td>-0.998507</td>\n",
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" <td>-0.991235</td>\n",
" <td>-0.991235</td>\n",
" <td>-0.991235</td>\n",
" <td>-0.991235</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
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" <td>-0.903614</td>\n",
" <td>-0.903614</td>\n",
" <td>-0.903614</td>\n",
" <td>-0.903614</td>\n",
" <td>-1.0</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.236838</td>\n",
" <td>-0.101680</td>\n",
" <td>0.144491</td>\n",
" <td>-0.539451</td>\n",
" <td>1.891420e-01</td>\n",
" <td>0.852390</td>\n",
" <td>0.251029</td>\n",
" <td>-0.102564</td>\n",
" <td>0.160377</td>\n",
" <td>-0.540230</td>\n",
" <td>0.226190</td>\n",
" <td>0.833333</td>\n",
" <td>0.237113</td>\n",
" <td>0.000000</td>\n",
" <td>0.048433</td>\n",
" <td>-0.500000</td>\n",
" <td>0.362637</td>\n",
" <td>0.925926</td>\n",
" <td>-0.076923</td>\n",
" <td>-0.293694</td>\n",
" <td>0.019900</td>\n",
" <td>-0.535354</td>\n",
" <td>0.072464</td>\n",
" <td>0.912281</td>\n",
" <td>-0.826087</td>\n",
" <td>-0.811861</td>\n",
" <td>-0.725191</td>\n",
" <td>-0.901961</td>\n",
" <td>-0.761905</td>\n",
" <td>-0.959596</td>\n",
" <td>-0.884058</td>\n",
" <td>-0.826611</td>\n",
" <td>-0.839287</td>\n",
" <td>-0.896057</td>\n",
" <td>-0.766042</td>\n",
" <td>-0.960291</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.000000</td>\n",
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" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891012</td>\n",
" <td>-0.891012</td>\n",
" <td>-0.891012</td>\n",
" <td>-0.891012</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
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" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>0.291405</td>\n",
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" <td>0.291405</td>\n",
" <td>0.291405</td>\n",
" <td>-1.0</td>\n",
" <td>0.243902</td>\n",
" <td>0.243902</td>\n",
" <td>0.243902</td>\n",
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" <td>-0.706450</td>\n",
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" <td>-0.340249</td>\n",
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" <td>-0.997947</td>\n",
" <td>-0.997947</td>\n",
" <td>-0.997947</td>\n",
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" <td>-0.988948</td>\n",
" <td>-0.988948</td>\n",
" <td>-0.988948</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.078189</td>\n",
" <td>-0.494587</td>\n",
" <td>-0.454228</td>\n",
" <td>-0.495292</td>\n",
" <td>-1.375661e-01</td>\n",
" <td>0.835283</td>\n",
" <td>-0.090535</td>\n",
" <td>-0.492308</td>\n",
" <td>-0.449686</td>\n",
" <td>-0.494253</td>\n",
" <td>-0.136905</td>\n",
" <td>0.833333</td>\n",
" <td>0.010309</td>\n",
" <td>-0.291667</td>\n",
" <td>-0.418803</td>\n",
" <td>-0.404762</td>\n",
" <td>0.252747</td>\n",
" <td>0.925926</td>\n",
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" <td>-0.553153</td>\n",
" <td>-0.482587</td>\n",
" <td>-0.535354</td>\n",
" <td>-0.246377</td>\n",
" <td>0.894737</td>\n",
" <td>-0.843478</td>\n",
" <td>-0.820041</td>\n",
" <td>-0.821883</td>\n",
" <td>-0.980392</td>\n",
" <td>-0.904762</td>\n",
" <td>-0.966330</td>\n",
" <td>-0.873174</td>\n",
" <td>-0.799242</td>\n",
" <td>-0.856110</td>\n",
" <td>-0.979689</td>\n",
" <td>-0.904177</td>\n",
" <td>-0.967019</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
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" <td>-1.000000</td>\n",
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" <td>0.357143</td>\n",
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" <td>-1.0</td>\n",
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" <td>2.678571e-01</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>-0.866667</td>\n",
" <td>-0.832311</td>\n",
" <td>-0.633588</td>\n",
" <td>-0.941176</td>\n",
" <td>-0.888889</td>\n",
" <td>-0.966330</td>\n",
" <td>-0.891551</td>\n",
" <td>-0.817431</td>\n",
" <td>-0.686446</td>\n",
" <td>-0.939068</td>\n",
" <td>-0.888823</td>\n",
" <td>-0.966710</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.605263</td>\n",
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" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.798110</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.010309</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
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" <td>-1.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>11.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>12.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>13.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>14.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
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" <td>-0.995894</td>\n",
" <td>-0.995894</td>\n",
" <td>-0.995894</td>\n",
" <td>-1.0</td>\n",
" <td>-0.990663</td>\n",
" <td>-0.990663</td>\n",
" <td>-0.990663</td>\n",
" <td>-0.990663</td>\n",
" <td>-1.0</td>\n",
" <td>-0.849747</td>\n",
" <td>-0.849747</td>\n",
" <td>-0.849747</td>\n",
" <td>-0.849747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.848193</td>\n",
" <td>-0.848193</td>\n",
" <td>-0.848193</td>\n",
" <td>-0.848193</td>\n",
" <td>-1.0</td>\n",
" <td>-0.961223</td>\n",
" <td>-0.961223</td>\n",
" <td>-0.961223</td>\n",
" <td>-0.961223</td>\n",
" <td>-1.0</td>\n",
" <td>0.118166</td>\n",
" <td>-0.404212</td>\n",
" <td>-0.360288</td>\n",
" <td>-0.569814</td>\n",
" <td>4.464286e-03</td>\n",
" <td>0.748120</td>\n",
" <td>0.086420</td>\n",
" <td>-0.384615</td>\n",
" <td>-0.358491</td>\n",
" <td>-0.563218</td>\n",
" <td>-0.011905</td>\n",
" <td>0.754386</td>\n",
" <td>0.168385</td>\n",
" <td>-0.208333</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.523810</td>\n",
" <td>0.318681</td>\n",
" <td>0.878788</td>\n",
" <td>-0.133903</td>\n",
" <td>-0.531532</td>\n",
" <td>-0.388060</td>\n",
" <td>-0.575758</td>\n",
" <td>-0.033816</td>\n",
" <td>0.807018</td>\n",
" <td>-0.826087</td>\n",
" <td>-0.877301</td>\n",
" <td>-0.801527</td>\n",
" <td>-0.921569</td>\n",
" <td>-0.801587</td>\n",
" <td>-0.952862</td>\n",
" <td>-0.850932</td>\n",
" <td>-0.869128</td>\n",
" <td>-0.830714</td>\n",
" <td>-0.918757</td>\n",
" <td>-0.802677</td>\n",
" <td>-0.952903</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
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" <td>-0.898089</td>\n",
" <td>-0.898089</td>\n",
" <td>-0.898089</td>\n",
" <td>-0.898089</td>\n",
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" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
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" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
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" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>0.064990</td>\n",
" <td>0.064990</td>\n",
" <td>0.064990</td>\n",
" <td>0.064990</td>\n",
" <td>-1.0</td>\n",
" <td>0.085366</td>\n",
" <td>0.085366</td>\n",
" <td>0.085366</td>\n",
" <td>0.085366</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
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" <td>-0.827801</td>\n",
" <td>-0.827801</td>\n",
" <td>-0.827801</td>\n",
" <td>-0.827801</td>\n",
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" <td>-0.874750</td>\n",
" <td>-0.874750</td>\n",
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" <td>-0.513422</td>\n",
" <td>-0.513422</td>\n",
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" <td>0.234043</td>\n",
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" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
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" <td>-0.471295</td>\n",
" <td>-0.471295</td>\n",
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" <td>-0.481481</td>\n",
" <td>-0.481481</td>\n",
" <td>-0.481481</td>\n",
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" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
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" <td>0.345679</td>\n",
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" <td>-0.993216</td>\n",
" <td>-0.993216</td>\n",
" <td>-0.993216</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>19.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>-1.000000</td>\n",
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" <td>-0.317073</td>\n",
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" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
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" <td>-0.923600</td>\n",
" <td>-0.923600</td>\n",
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" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.428571</td>\n",
" <td>0.428571</td>\n",
" <td>0.428571</td>\n",
" <td>0.428571</td>\n",
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" <td>-0.905166</td>\n",
" <td>-0.905166</td>\n",
" <td>-0.905166</td>\n",
" <td>-0.905166</td>\n",
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" <td>-0.748401</td>\n",
" <td>-0.748401</td>\n",
" <td>-0.748401</td>\n",
" <td>-0.748401</td>\n",
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" <td>-0.978972</td>\n",
" <td>-0.978972</td>\n",
" <td>-0.978972</td>\n",
" <td>-0.978972</td>\n",
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" <td>-0.869646</td>\n",
" <td>-0.869646</td>\n",
" <td>-0.869646</td>\n",
" <td>-0.869646</td>\n",
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" <td>-0.023061</td>\n",
" <td>-0.023061</td>\n",
" <td>-0.023061</td>\n",
" <td>-0.023061</td>\n",
" <td>-1.0</td>\n",
" <td>0.036585</td>\n",
" <td>0.036585</td>\n",
" <td>0.036585</td>\n",
" <td>0.036585</td>\n",
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" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
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" <td>1.000000</td>\n",
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" <td>-0.714175</td>\n",
" <td>-0.714175</td>\n",
" <td>-0.714175</td>\n",
" <td>-0.714175</td>\n",
" <td>-1.0</td>\n",
" <td>-0.609959</td>\n",
" <td>-0.609959</td>\n",
" <td>-0.609959</td>\n",
" <td>-0.609959</td>\n",
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" <td>-0.807923</td>\n",
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" <td>-0.170732</td>\n",
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" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
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" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
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" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
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" <td>-0.999244</td>\n",
" <td>-0.999244</td>\n",
" <td>-0.999244</td>\n",
" <td>-0.999244</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
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" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
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" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
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" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
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" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
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" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
" <td>0.142857</td>\n",
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" <td>-0.998880</td>\n",
" <td>-0.998880</td>\n",
" <td>-0.998880</td>\n",
" <td>-0.998880</td>\n",
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" <td>-0.996570</td>\n",
" <td>-0.996570</td>\n",
" <td>-0.996570</td>\n",
" <td>-0.996570</td>\n",
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" <td>-0.874659</td>\n",
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" <td>0.123077</td>\n",
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" <td>-0.186441</td>\n",
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" <td>0.842105</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>20.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>22.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>23.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>24.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>25.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>26.0</td>\n",
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" <td>-0.874858</td>\n",
" <td>-0.874858</td>\n",
" <td>-0.874858</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
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" <td>-0.455274</td>\n",
" <td>-0.455274</td>\n",
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" <td>-0.592593</td>\n",
" <td>-0.592593</td>\n",
" <td>-0.592593</td>\n",
" <td>-0.592593</td>\n",
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" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
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" <td>0.345679</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>29.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>30.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.605263</td>\n",
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" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
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" <td>-0.779838</td>\n",
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" <td>-0.779838</td>\n",
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" <td>-0.988658</td>\n",
" <td>-0.988658</td>\n",
" <td>-0.988658</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
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" <td>0.234043</td>\n",
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" <td>-0.529986</td>\n",
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" <td>0.890907</td>\n",
" <td>-0.078189</td>\n",
" <td>-0.487179</td>\n",
" <td>-0.556604</td>\n",
" <td>-0.494253</td>\n",
" <td>-0.166667</td>\n",
" <td>0.947368</td>\n",
" <td>-0.085911</td>\n",
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" <td>-0.512821</td>\n",
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" <td>0.164835</td>\n",
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" <td>-0.256039</td>\n",
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" <td>-0.817460</td>\n",
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" <td>-0.816149</td>\n",
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" <td>-0.816042</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>31.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>-0.946036</td>\n",
" <td>-0.946036</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>32.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
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" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.950460</td>\n",
" <td>-0.950460</td>\n",
" <td>-0.950460</td>\n",
" <td>-0.950460</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.949474</td>\n",
" <td>-0.949474</td>\n",
" <td>-0.949474</td>\n",
" <td>-0.949474</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>-0.280922</td>\n",
" <td>-0.280922</td>\n",
" <td>-0.280922</td>\n",
" <td>-0.280922</td>\n",
" <td>-1.0</td>\n",
" <td>-0.329268</td>\n",
" <td>-0.329268</td>\n",
" <td>-0.329268</td>\n",
" <td>-0.329268</td>\n",
" <td>-1.0</td>\n",
" <td>-0.962359</td>\n",
" <td>-0.962359</td>\n",
" <td>-0.962359</td>\n",
" <td>-0.962359</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.840093</td>\n",
" <td>-0.840093</td>\n",
" <td>-0.840093</td>\n",
" <td>-0.840093</td>\n",
" <td>-1.0</td>\n",
" <td>-0.826763</td>\n",
" <td>-0.826763</td>\n",
" <td>-0.826763</td>\n",
" <td>-0.826763</td>\n",
" <td>-1.0</td>\n",
" <td>-0.881553</td>\n",
" <td>-0.881553</td>\n",
" <td>-0.881553</td>\n",
" <td>-0.881553</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.866163</td>\n",
" <td>-0.866163</td>\n",
" <td>-0.866163</td>\n",
" <td>-0.866163</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.455274</td>\n",
" <td>-0.455274</td>\n",
" <td>-0.455274</td>\n",
" <td>-0.455274</td>\n",
" <td>-1.0</td>\n",
" <td>-0.611111</td>\n",
" <td>-0.611111</td>\n",
" <td>-0.611111</td>\n",
" <td>-0.611111</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>-0.171429</td>\n",
" <td>-0.171429</td>\n",
" <td>-0.171429</td>\n",
" <td>-0.171429</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-1.0</td>\n",
" <td>-0.992759</td>\n",
" <td>-0.992759</td>\n",
" <td>-0.992759</td>\n",
" <td>-0.992759</td>\n",
" <td>-1.0</td>\n",
" <td>-0.830673</td>\n",
" <td>-0.830673</td>\n",
" <td>-0.830673</td>\n",
" <td>-0.830673</td>\n",
" <td>-1.0</td>\n",
" <td>-0.925301</td>\n",
" <td>-0.925301</td>\n",
" <td>-0.925301</td>\n",
" <td>-0.925301</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986740</td>\n",
" <td>-0.986740</td>\n",
" <td>-0.986740</td>\n",
" <td>-0.986740</td>\n",
" <td>-1.0</td>\n",
" <td>-0.090698</td>\n",
" <td>-0.457045</td>\n",
" <td>-0.113021</td>\n",
" <td>-0.501594</td>\n",
" <td>1.252526e-01</td>\n",
" <td>0.744919</td>\n",
" <td>-0.098765</td>\n",
" <td>-0.461538</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.517241</td>\n",
" <td>0.071429</td>\n",
" <td>0.763158</td>\n",
" <td>-0.072165</td>\n",
" <td>-0.250000</td>\n",
" <td>-0.119658</td>\n",
" <td>-0.464286</td>\n",
" <td>0.362637</td>\n",
" <td>0.838384</td>\n",
" <td>-0.239316</td>\n",
" <td>-0.486486</td>\n",
" <td>-0.119403</td>\n",
" <td>-0.484848</td>\n",
" <td>0.260870</td>\n",
" <td>0.842105</td>\n",
" <td>-0.730435</td>\n",
" <td>-0.785276</td>\n",
" <td>-0.717557</td>\n",
" <td>-0.882353</td>\n",
" <td>-0.607143</td>\n",
" <td>-0.898990</td>\n",
" <td>-0.784348</td>\n",
" <td>-0.750153</td>\n",
" <td>-0.789978</td>\n",
" <td>-0.878136</td>\n",
" <td>-0.609948</td>\n",
" <td>-0.899078</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 GENDER DISEASE GROUPING 1 \\\n",
"0 0.0 1.0 0.0 0.0 \n",
"4 2.0 0.0 0.0 0.0 \n",
"8 3.0 0.0 1.0 0.0 \n",
"13 4.0 0.0 0.0 0.0 \n",
"18 5.0 0.0 0.0 0.0 \n",
"23 6.0 1.0 1.0 0.0 \n",
"28 7.0 0.0 0.0 0.0 \n",
"33 8.0 0.0 0.0 0.0 \n",
"38 9.0 1.0 0.0 0.0 \n",
"43 10.0 1.0 0.0 0.0 \n",
"48 11.0 1.0 0.0 0.0 \n",
"51 12.0 0.0 1.0 0.0 \n",
"56 13.0 0.0 0.0 0.0 \n",
"60 14.0 1.0 1.0 0.0 \n",
"62 15.0 0.0 0.0 0.0 \n",
"66 16.0 0.0 0.0 0.0 \n",
"71 18.0 0.0 0.0 0.0 \n",
"73 19.0 1.0 0.0 0.0 \n",
"77 20.0 0.0 1.0 0.0 \n",
"82 21.0 1.0 0.0 0.0 \n",
"87 22.0 1.0 0.0 0.0 \n",
"92 23.0 0.0 1.0 0.0 \n",
"97 24.0 1.0 1.0 0.0 \n",
"99 25.0 1.0 0.0 0.0 \n",
"104 26.0 0.0 0.0 0.0 \n",
"109 28.0 0.0 0.0 0.0 \n",
"110 29.0 1.0 0.0 0.0 \n",
"112 30.0 1.0 1.0 0.0 \n",
"117 31.0 1.0 0.0 0.0 \n",
"119 32.0 0.0 1.0 0.0 \n",
"\n",
" DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"23 0.0 0.0 0.0 \n",
"28 0.0 0.0 0.0 \n",
"33 0.0 0.0 0.0 \n",
"38 0.0 0.0 0.0 \n",
"43 0.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 \n",
"51 0.0 0.0 0.0 \n",
"56 0.0 0.0 0.0 \n",
"60 0.0 0.0 0.0 \n",
"62 0.0 0.0 0.0 \n",
"66 0.0 0.0 0.0 \n",
"71 0.0 0.0 0.0 \n",
"73 0.0 0.0 0.0 \n",
"77 0.0 0.0 0.0 \n",
"82 0.0 0.0 0.0 \n",
"87 0.0 0.0 0.0 \n",
"92 0.0 0.0 0.0 \n",
"97 0.0 0.0 0.0 \n",
"99 0.0 0.0 0.0 \n",
"104 0.0 0.0 0.0 \n",
"109 0.0 0.0 0.0 \n",
"110 0.0 1.0 1.0 \n",
"112 0.0 0.0 0.0 \n",
"117 0.0 0.0 0.0 \n",
"119 0.0 0.0 0.0 \n",
"\n",
" DISEASE GROUPING 5 DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"0 1.0 1.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 1.0 1.0 \n",
"13 0.0 0.0 0.0 0.0 1.0 \n",
"18 0.0 0.0 0.0 0.0 1.0 \n",
"23 0.0 0.0 0.0 1.0 1.0 \n",
"28 0.0 0.0 1.0 0.0 1.0 \n",
"33 0.0 0.0 0.0 0.0 0.0 \n",
"38 0.0 0.0 0.0 0.0 0.0 \n",
"43 0.0 0.0 0.0 0.0 1.0 \n",
"48 0.0 0.0 0.0 0.0 0.0 \n",
"51 0.0 0.0 0.0 0.0 1.0 \n",
"56 0.0 0.0 0.0 0.0 0.0 \n",
"60 0.0 0.0 0.0 1.0 1.0 \n",
"62 0.0 0.0 0.0 1.0 1.0 \n",
"66 0.0 0.0 0.0 0.0 0.0 \n",
"71 0.0 0.0 0.0 0.0 0.0 \n",
"73 0.0 0.0 1.0 1.0 1.0 \n",
"77 0.0 0.0 0.0 0.0 0.0 \n",
"82 0.0 0.0 0.0 0.0 1.0 \n",
"87 1.0 0.0 1.0 0.0 1.0 \n",
"92 0.0 0.0 0.0 0.0 0.0 \n",
"97 0.0 0.0 0.0 0.0 1.0 \n",
"99 0.0 0.0 0.0 0.0 1.0 \n",
"104 0.0 0.0 0.0 0.0 1.0 \n",
"109 0.0 0.0 0.0 0.0 1.0 \n",
"110 1.0 0.0 1.0 0.0 1.0 \n",
"112 0.0 0.0 0.0 0.0 1.0 \n",
"117 0.0 0.0 0.0 0.0 1.0 \n",
"119 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"0 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"4 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"8 -0.263158 -0.263158 -0.263158 -0.263158 -1.0 \n",
"13 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"18 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"23 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"28 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"33 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"38 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"43 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"48 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"51 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"56 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"60 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"62 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"66 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"71 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"73 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"77 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"82 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"87 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"92 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"97 0.263158 0.263158 0.263158 0.263158 -1.0 \n",
"99 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"104 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"112 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"117 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"119 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"0 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"4 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"8 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"13 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"18 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"23 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"28 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"33 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"38 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"43 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"51 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"56 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"60 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"62 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"66 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"71 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"73 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"77 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"82 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"87 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"92 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"97 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"99 -0.960784 -0.960784 -0.960784 -0.960784 \n",
"104 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"112 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"117 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"119 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"0 -1.0 -1.000000 -1.000000 -1.000000 \n",
"4 -1.0 -1.000000 -1.000000 -1.000000 \n",
"8 -1.0 -1.000000 -1.000000 -1.000000 \n",
"13 -1.0 -1.000000 -1.000000 -1.000000 \n",
"18 -1.0 -1.000000 -1.000000 -1.000000 \n",
"23 -1.0 -1.000000 -1.000000 -1.000000 \n",
"28 -1.0 -1.000000 -1.000000 -1.000000 \n",
"33 -1.0 -1.000000 -1.000000 -1.000000 \n",
"38 -1.0 -1.000000 -1.000000 -1.000000 \n",
"43 -1.0 -1.000000 -1.000000 -1.000000 \n",
"48 -1.0 -1.000000 -1.000000 -1.000000 \n",
"51 -1.0 -1.000000 -1.000000 -1.000000 \n",
"56 -1.0 -1.000000 -1.000000 -1.000000 \n",
"60 -1.0 -0.895288 -0.895288 -0.895288 \n",
"62 -1.0 -1.000000 -1.000000 -1.000000 \n",
"66 -1.0 -1.000000 -1.000000 -1.000000 \n",
"71 -1.0 -1.000000 -1.000000 -1.000000 \n",
"73 -1.0 -1.000000 -1.000000 -1.000000 \n",
"77 -1.0 -1.000000 -1.000000 -1.000000 \n",
"82 -1.0 -0.884817 -0.884817 -0.884817 \n",
"87 -1.0 -0.821990 -0.821990 -0.821990 \n",
"92 -1.0 -1.000000 -1.000000 -1.000000 \n",
"97 -1.0 -1.000000 -1.000000 -1.000000 \n",
"99 -1.0 -1.000000 -1.000000 -1.000000 \n",
"104 -1.0 -1.000000 -1.000000 -1.000000 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.0 -1.000000 -1.000000 -1.000000 \n",
"112 -1.0 -1.000000 -1.000000 -1.000000 \n",
"117 -1.0 -1.000000 -1.000000 -1.000000 \n",
"119 -1.0 -1.000000 -1.000000 -1.000000 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"0 -1.000000 -1.0 -0.317073 -0.317073 \n",
"4 -1.000000 -1.0 -0.317073 -0.317073 \n",
"8 -1.000000 -1.0 -0.317073 -0.317073 \n",
"13 -1.000000 -1.0 -0.317073 -0.317073 \n",
"18 -1.000000 -1.0 -0.317073 -0.317073 \n",
"23 -1.000000 -1.0 -0.317073 -0.317073 \n",
"28 -1.000000 -1.0 -0.317073 -0.317073 \n",
"33 -1.000000 -1.0 -0.317073 -0.317073 \n",
"38 -1.000000 -1.0 -0.317073 -0.317073 \n",
"43 -1.000000 -1.0 -0.317073 -0.317073 \n",
"48 -1.000000 -1.0 -0.317073 -0.317073 \n",
"51 -1.000000 -1.0 -0.317073 -0.317073 \n",
"56 -1.000000 -1.0 -0.317073 -0.317073 \n",
"60 -0.895288 -1.0 -0.317073 -0.317073 \n",
"62 -1.000000 -1.0 -0.317073 -0.317073 \n",
"66 -1.000000 -1.0 -0.317073 -0.317073 \n",
"71 -1.000000 -1.0 -0.317073 -0.317073 \n",
"73 -1.000000 -1.0 -0.317073 -0.317073 \n",
"77 -1.000000 -1.0 -0.317073 -0.317073 \n",
"82 -0.884817 -1.0 -0.317073 -0.317073 \n",
"87 -0.821990 -1.0 -0.317073 -0.317073 \n",
"92 -1.000000 -1.0 -0.317073 -0.317073 \n",
"97 -1.000000 -1.0 -0.317073 -0.317073 \n",
"99 -1.000000 -1.0 -0.333333 -0.333333 \n",
"104 -1.000000 -1.0 -0.317073 -0.317073 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.000000 -1.0 -0.317073 -0.317073 \n",
"112 -1.000000 -1.0 -0.317073 -0.317073 \n",
"117 -1.000000 -1.0 -0.317073 -0.317073 \n",
"119 -1.000000 -1.0 -0.317073 -0.317073 \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN \\\n",
"0 -0.317073 -0.317073 -1.0 -0.317073 \n",
"4 -0.317073 -0.317073 -1.0 -0.317073 \n",
"8 -0.317073 -0.317073 -1.0 -0.317073 \n",
"13 -0.317073 -0.317073 -1.0 -0.317073 \n",
"18 -0.317073 -0.317073 -1.0 -0.317073 \n",
"23 -0.317073 -0.317073 -1.0 -0.317073 \n",
"28 -0.317073 -0.317073 -1.0 -0.317073 \n",
"33 -0.317073 -0.317073 -1.0 -0.317073 \n",
"38 -0.317073 -0.317073 -1.0 -0.317073 \n",
"43 -0.317073 -0.317073 -1.0 -0.317073 \n",
"48 -0.317073 -0.317073 -1.0 -0.317073 \n",
"51 -0.317073 -0.317073 -1.0 -0.317073 \n",
"56 -0.317073 -0.317073 -1.0 -0.317073 \n",
"60 -0.317073 -0.317073 -1.0 -0.268293 \n",
"62 -0.317073 -0.317073 -1.0 -0.512195 \n",
"66 -0.317073 -0.317073 -1.0 -0.317073 \n",
"71 -0.317073 -0.317073 -1.0 -0.317073 \n",
"73 -0.317073 -0.317073 -1.0 -0.317073 \n",
"77 -0.317073 -0.317073 -1.0 -0.317073 \n",
"82 -0.317073 -0.317073 -1.0 -0.292683 \n",
"87 -0.317073 -0.317073 -1.0 -0.073171 \n",
"92 -0.317073 -0.317073 -1.0 -0.317073 \n",
"97 -0.317073 -0.317073 -1.0 -0.512195 \n",
"99 -0.333333 -0.333333 -1.0 -0.317073 \n",
"104 -0.317073 -0.317073 -1.0 -0.317073 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.317073 -0.317073 -1.0 -0.317073 \n",
"112 -0.317073 -0.317073 -1.0 -0.365854 \n",
"117 -0.317073 -0.317073 -1.0 -0.560976 \n",
"119 -0.317073 -0.317073 -1.0 -0.317073 \n",
"\n",
" BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF \\\n",
"0 -0.317073 -0.317073 -0.317073 -1.0 \n",
"4 -0.317073 -0.317073 -0.317073 -1.0 \n",
"8 -0.317073 -0.317073 -0.317073 -1.0 \n",
"13 -0.317073 -0.317073 -0.317073 -1.0 \n",
"18 -0.317073 -0.317073 -0.317073 -1.0 \n",
"23 -0.317073 -0.317073 -0.317073 -1.0 \n",
"28 -0.317073 -0.317073 -0.317073 -1.0 \n",
"33 -0.317073 -0.317073 -0.317073 -1.0 \n",
"38 -0.317073 -0.317073 -0.317073 -1.0 \n",
"43 -0.317073 -0.317073 -0.317073 -1.0 \n",
"48 -0.317073 -0.317073 -0.317073 -1.0 \n",
"51 -0.317073 -0.317073 -0.317073 -1.0 \n",
"56 -0.317073 -0.317073 -0.317073 -1.0 \n",
"60 -0.268293 -0.268293 -0.268293 -1.0 \n",
"62 -0.512195 -0.512195 -0.512195 -1.0 \n",
"66 -0.317073 -0.317073 -0.317073 -1.0 \n",
"71 -0.317073 -0.317073 -0.317073 -1.0 \n",
"73 -0.317073 -0.317073 -0.317073 -1.0 \n",
"77 -0.317073 -0.317073 -0.317073 -1.0 \n",
"82 -0.292683 -0.292683 -0.292683 -1.0 \n",
"87 -0.073171 -0.073171 -0.073171 -1.0 \n",
"92 -0.317073 -0.317073 -0.317073 -1.0 \n",
"97 -0.512195 -0.512195 -0.512195 -1.0 \n",
"99 -0.317073 -0.317073 -0.317073 -1.0 \n",
"104 -0.317073 -0.317073 -0.317073 -1.0 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.317073 -0.317073 -0.317073 -1.0 \n",
"112 -0.365854 -0.365854 -0.365854 -1.0 \n",
"117 -0.560976 -0.560976 -0.560976 -1.0 \n",
"119 -0.317073 -0.317073 -0.317073 -1.0 \n",
"\n",
" BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX \\\n",
"0 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"4 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"8 -0.972789 -0.972789 -0.972789 -0.972789 \n",
"13 -0.935113 -0.935113 -0.935113 -0.935113 \n",
"18 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"23 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"28 -0.966510 -0.966510 -0.966510 -0.966510 \n",
"33 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"38 -0.944357 -0.944357 -0.944357 -0.944357 \n",
"43 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"48 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"51 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"56 -0.944532 -0.944532 -0.944532 -0.944532 \n",
"60 -0.953951 -0.953951 -0.953951 -0.953951 \n",
"62 -0.957091 -0.957091 -0.957091 -0.957091 \n",
"66 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"71 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"73 -0.923600 -0.923600 -0.923600 -0.923600 \n",
"77 -0.965463 -0.965463 -0.965463 -0.965463 \n",
"82 -0.851736 -0.851736 -0.851736 -0.851736 \n",
"87 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"92 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"97 -0.969649 -0.969649 -0.969649 -0.969649 \n",
"99 -0.924763 -0.924763 -0.924763 -0.924763 \n",
"104 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"112 -0.972266 -0.972266 -0.972266 -0.972266 \n",
"117 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"119 -0.938950 -0.938950 -0.938950 -0.938950 \n",
"\n",
" BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX \\\n",
"0 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"8 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"13 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"18 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"23 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"28 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"33 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"38 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"43 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"48 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"51 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"56 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"60 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"62 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"66 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"71 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"73 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"77 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"82 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"87 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"92 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"97 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"99 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"104 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"112 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"117 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"119 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX \\\n",
"0 -1.0 0.183673 0.183673 0.183673 0.183673 \n",
"4 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"8 -1.0 0.326531 0.326531 0.326531 0.326531 \n",
"13 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"18 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"23 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"28 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"33 -1.0 0.081633 0.081633 0.081633 0.081633 \n",
"38 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"43 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"48 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"51 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"56 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"60 -1.0 0.061224 0.061224 0.061224 0.061224 \n",
"62 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"66 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"71 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"73 -1.0 0.428571 0.428571 0.428571 0.428571 \n",
"77 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"82 -1.0 0.260204 0.260204 0.260204 0.260204 \n",
"87 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"92 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"97 -1.0 0.081633 0.081633 0.081633 0.081633 \n",
"99 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"104 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"112 -1.0 0.397959 0.397959 0.397959 0.397959 \n",
"117 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"119 -1.0 0.357143 0.357143 0.357143 0.357143 \n",
"\n",
" CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN \\\n",
"0 -1.0 -0.868365 -0.868365 -0.868365 \n",
"4 -1.0 -0.912243 -0.912243 -0.912243 \n",
"8 -1.0 -0.968861 -0.968861 -0.968861 \n",
"13 -1.0 -0.913659 -0.913659 -0.913659 \n",
"18 -1.0 -0.891012 -0.891012 -0.891012 \n",
"23 -1.0 -0.944798 -0.944798 -0.944798 \n",
"28 -1.0 -0.912243 -0.912243 -0.912243 \n",
"33 -1.0 -0.868365 -0.868365 -0.868365 \n",
"38 -1.0 -0.923567 -0.923567 -0.923567 \n",
"43 -1.0 -0.872611 -0.872611 -0.872611 \n",
"48 -1.0 -0.849965 -0.849965 -0.849965 \n",
"51 -1.0 -0.949045 -0.949045 -0.949045 \n",
"56 -1.0 -0.871196 -0.871196 -0.871196 \n",
"60 -1.0 -0.842887 -0.842887 -0.842887 \n",
"62 -1.0 -0.893843 -0.893843 -0.893843 \n",
"66 -1.0 -0.883935 -0.883935 -0.883935 \n",
"71 -1.0 -0.898089 -0.898089 -0.898089 \n",
"73 -1.0 -0.905166 -0.905166 -0.905166 \n",
"77 -1.0 -0.907997 -0.907997 -0.907997 \n",
"82 -1.0 -0.897381 -0.897381 -0.897381 \n",
"87 -1.0 -0.883935 -0.883935 -0.883935 \n",
"92 -1.0 -0.934890 -0.934890 -0.934890 \n",
"97 -1.0 -0.923567 -0.923567 -0.923567 \n",
"99 -1.0 -0.877801 -0.877801 -0.877801 \n",
"104 -1.0 -0.913659 -0.913659 -0.913659 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.0 -0.814579 -0.814579 -0.814579 \n",
"112 -1.0 -0.934183 -0.934183 -0.934183 \n",
"117 -1.0 -0.813163 -0.813163 -0.813163 \n",
"119 -1.0 -0.950460 -0.950460 -0.950460 \n",
"\n",
" CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX \\\n",
"0 -0.868365 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"4 -0.912243 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"8 -0.968861 -1.0 -0.194030 -0.194030 -0.194030 -0.194030 \n",
"13 -0.913659 -1.0 -0.829424 -0.829424 -0.829424 -0.829424 \n",
"18 -0.891012 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"23 -0.944798 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"28 -0.912243 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"33 -0.868365 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"38 -0.923567 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"43 -0.872611 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"48 -0.849965 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"51 -0.949045 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"56 -0.871196 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"60 -0.842887 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"62 -0.893843 -1.0 -0.437100 -0.437100 -0.437100 -0.437100 \n",
"66 -0.883935 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"71 -0.898089 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"73 -0.905166 -1.0 -0.748401 -0.748401 -0.748401 -0.748401 \n",
"77 -0.907997 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"82 -0.897381 -1.0 -0.696162 -0.696162 -0.696162 -0.696162 \n",
"87 -0.883935 -1.0 -0.773987 -0.773987 -0.773987 -0.773987 \n",
"92 -0.934890 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"97 -0.923567 -1.0 -0.880597 -0.880597 -0.880597 -0.880597 \n",
"99 -0.877801 -1.0 -0.647477 -0.647477 -0.647477 -0.647477 \n",
"104 -0.913659 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"109 NaN NaN NaN NaN NaN NaN \n",
"110 -0.814579 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"112 -0.934183 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"117 -0.813163 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"119 -0.950460 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 \n",
"\n",
" FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF \\\n",
"0 -1.0 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 \n",
"4 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"8 -1.0 -0.316589 -0.316589 -0.316589 -0.316589 -1.0 \n",
"13 -1.0 -0.938084 -0.938084 -0.938084 -0.938084 -1.0 \n",
"18 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"23 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"28 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"33 -1.0 -0.990654 -0.990654 -0.990654 -0.990654 -1.0 \n",
"38 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"43 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"48 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"51 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"56 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"60 -1.0 -0.967290 -0.967290 -0.967290 -0.967290 -1.0 \n",
"62 -1.0 -0.766355 -0.766355 -0.766355 -0.766355 -1.0 \n",
"66 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"71 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"73 -1.0 -0.978972 -0.978972 -0.978972 -0.978972 -1.0 \n",
"77 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"82 -1.0 -0.881133 -0.881133 -0.881133 -0.881133 -1.0 \n",
"87 -1.0 -0.989486 -0.989486 -0.989486 -0.989486 -1.0 \n",
"92 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"97 -1.0 -0.996495 -0.996495 -0.996495 -0.996495 -1.0 \n",
"99 -1.0 -0.948403 -0.948403 -0.948403 -0.948403 -1.0 \n",
"104 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"109 NaN NaN NaN NaN NaN NaN \n",
"110 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"112 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"117 -1.0 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 \n",
"119 -1.0 -0.949474 -0.949474 -0.949474 -0.949474 -1.0 \n",
"\n",
" GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF \\\n",
"0 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"4 -0.780261 -0.780261 -0.780261 -0.780261 -1.0 \n",
"8 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"13 -0.851024 -0.851024 -0.851024 -0.851024 -1.0 \n",
"18 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"23 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"28 -0.851024 -0.851024 -0.851024 -0.851024 -1.0 \n",
"33 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"38 -0.897579 -0.897579 -0.897579 -0.897579 -1.0 \n",
"43 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"48 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"51 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"56 -0.813780 -0.813780 -0.813780 -0.813780 -1.0 \n",
"60 -0.851024 -0.851024 -0.851024 -0.851024 -1.0 \n",
"62 -0.810056 -0.810056 -0.810056 -0.810056 -1.0 \n",
"66 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"71 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"73 -0.869646 -0.869646 -0.869646 -0.869646 -1.0 \n",
"77 -0.817505 -0.817505 -0.817505 -0.817505 -1.0 \n",
"82 -0.884544 -0.884544 -0.884544 -0.884544 -1.0 \n",
"87 -0.918063 -0.918063 -0.918063 -0.918063 -1.0 \n",
"92 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"97 -0.735568 -0.735568 -0.735568 -0.735568 -1.0 \n",
"99 -0.817505 -0.817505 -0.817505 -0.817505 -1.0 \n",
"104 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"112 -0.890130 -0.890130 -0.890130 -0.890130 -1.0 \n",
"117 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"119 -0.891993 -0.891993 -0.891993 -0.891993 -1.0 \n",
"\n",
" HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"0 0.090147 0.090147 0.090147 0.090147 \n",
"4 0.144654 0.144654 0.144654 0.144654 \n",
"8 -0.203354 -0.203354 -0.203354 -0.203354 \n",
"13 0.358491 0.358491 0.358491 0.358491 \n",
"18 0.291405 0.291405 0.291405 0.291405 \n",
"23 -0.471698 -0.471698 -0.471698 -0.471698 \n",
"28 -0.052411 -0.052411 -0.052411 -0.052411 \n",
"33 -0.039832 -0.039832 -0.039832 -0.039832 \n",
"38 -0.025157 -0.025157 -0.025157 -0.025157 \n",
"43 -0.224319 -0.224319 -0.224319 -0.224319 \n",
"48 -0.303983 -0.303983 -0.303983 -0.303983 \n",
"51 -0.044025 -0.044025 -0.044025 -0.044025 \n",
"56 0.010482 0.010482 0.010482 0.010482 \n",
"60 -0.169811 -0.169811 -0.169811 -0.169811 \n",
"62 -0.324948 -0.324948 -0.324948 -0.324948 \n",
"66 0.146751 0.146751 0.146751 0.146751 \n",
"71 0.064990 0.064990 0.064990 0.064990 \n",
"73 -0.023061 -0.023061 -0.023061 -0.023061 \n",
"77 -0.228512 -0.228512 -0.228512 -0.228512 \n",
"82 -0.197065 -0.197065 -0.197065 -0.197065 \n",
"87 0.232704 0.232704 0.232704 0.232704 \n",
"92 -0.044025 -0.044025 -0.044025 -0.044025 \n",
"97 -0.228512 -0.228512 -0.228512 -0.228512 \n",
"99 -0.134871 -0.134871 -0.134871 -0.134871 \n",
"104 0.140461 0.140461 0.140461 0.140461 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.190776 -0.190776 -0.190776 -0.190776 \n",
"112 -0.220126 -0.220126 -0.220126 -0.220126 \n",
"117 -0.094340 -0.094340 -0.094340 -0.094340 \n",
"119 -0.280922 -0.280922 -0.280922 -0.280922 \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN \\\n",
"0 -1.0 0.109756 0.109756 0.109756 \n",
"4 -1.0 0.158537 0.158537 0.158537 \n",
"8 -1.0 -0.219512 -0.219512 -0.219512 \n",
"13 -1.0 0.304878 0.304878 0.304878 \n",
"18 -1.0 0.243902 0.243902 0.243902 \n",
"23 -1.0 -0.475610 -0.475610 -0.475610 \n",
"28 -1.0 -0.024390 -0.024390 -0.024390 \n",
"33 -1.0 -0.012195 -0.012195 -0.012195 \n",
"38 -1.0 -0.054878 -0.054878 -0.054878 \n",
"43 -1.0 -0.292683 -0.292683 -0.292683 \n",
"48 -1.0 -0.353659 -0.353659 -0.353659 \n",
"51 -1.0 -0.091463 -0.091463 -0.091463 \n",
"56 -1.0 0.012195 0.012195 0.012195 \n",
"60 -1.0 -0.207317 -0.207317 -0.207317 \n",
"62 -1.0 -0.390244 -0.390244 -0.390244 \n",
"66 -1.0 0.103659 0.103659 0.103659 \n",
"71 -1.0 0.085366 0.085366 0.085366 \n",
"73 -1.0 0.036585 0.036585 0.036585 \n",
"77 -1.0 -0.256098 -0.256098 -0.256098 \n",
"82 -1.0 -0.164634 -0.164634 -0.164634 \n",
"87 -1.0 0.146341 0.146341 0.146341 \n",
"92 -1.0 -0.048780 -0.048780 -0.048780 \n",
"97 -1.0 -0.256098 -0.256098 -0.256098 \n",
"99 -1.0 -0.215447 -0.215447 -0.215447 \n",
"104 -1.0 0.109756 0.109756 0.109756 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.0 -0.231707 -0.231707 -0.231707 \n",
"112 -1.0 -0.292683 -0.292683 -0.292683 \n",
"117 -1.0 -0.195122 -0.195122 -0.195122 \n",
"119 -1.0 -0.329268 -0.329268 -0.329268 \n",
"\n",
" HEMOGLOBIN_MAX HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN \\\n",
"0 0.109756 -1.0 -0.932246 -0.932246 -0.932246 \n",
"4 0.158537 -1.0 -0.959849 -0.959849 -0.959849 \n",
"8 -0.219512 -1.0 -0.959849 -0.959849 -0.959849 \n",
"13 0.304878 -1.0 -0.959849 -0.959849 -0.959849 \n",
"18 0.243902 -1.0 -0.959849 -0.959849 -0.959849 \n",
"23 -0.475610 -1.0 -0.959849 -0.959849 -0.959849 \n",
"28 -0.024390 -1.0 -0.967378 -0.967378 -0.967378 \n",
"33 -0.012195 -1.0 -0.959849 -0.959849 -0.959849 \n",
"38 -0.054878 -1.0 -0.959849 -0.959849 -0.959849 \n",
"43 -0.292683 -1.0 -0.957340 -0.957340 -0.957340 \n",
"48 -0.353659 -1.0 -0.907152 -0.907152 -0.907152 \n",
"51 -0.091463 -1.0 -0.958595 -0.958595 -0.958595 \n",
"56 0.012195 -1.0 -0.959849 -0.959849 -0.959849 \n",
"60 -0.207317 -1.0 -0.932246 -0.932246 -0.932246 \n",
"62 -0.390244 -1.0 -0.959849 -0.959849 -0.959849 \n",
"66 0.103659 -1.0 -0.958595 -0.958595 -0.958595 \n",
"71 0.085366 -1.0 -0.959849 -0.959849 -0.959849 \n",
"73 0.036585 -1.0 -0.959849 -0.959849 -0.959849 \n",
"77 -0.256098 -1.0 -0.959849 -0.959849 -0.959849 \n",
"82 -0.164634 -1.0 -0.959849 -0.959849 -0.959849 \n",
"87 0.146341 -1.0 -0.959849 -0.959849 -0.959849 \n",
"92 -0.048780 -1.0 -0.959849 -0.959849 -0.959849 \n",
"97 -0.256098 -1.0 -0.932246 -0.932246 -0.932246 \n",
"99 -0.215447 -1.0 -0.959849 -0.959849 -0.959849 \n",
"104 0.109756 -1.0 -0.959849 -0.959849 -0.959849 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -0.231707 -1.0 -0.606023 -0.606023 -0.606023 \n",
"112 -0.292683 -1.0 -0.959849 -0.959849 -0.959849 \n",
"117 -0.195122 -1.0 -0.959849 -0.959849 -0.959849 \n",
"119 -0.329268 -1.0 -0.962359 -0.962359 -0.962359 \n",
"\n",
" INR_MAX INR_DIFF LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN \\\n",
"0 -0.932246 -1.0 1.000000 1.000000 1.000000 \n",
"4 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"8 -0.959849 -1.0 -0.828421 -0.828421 -0.828421 \n",
"13 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"18 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"23 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"28 -0.967378 -1.0 1.000000 1.000000 1.000000 \n",
"33 -0.959849 -1.0 -0.952572 -0.952572 -0.952572 \n",
"38 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"43 -0.957340 -1.0 1.000000 1.000000 1.000000 \n",
"48 -0.907152 -1.0 1.000000 1.000000 1.000000 \n",
"51 -0.958595 -1.0 0.028537 0.028537 0.028537 \n",
"56 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"60 -0.932246 -1.0 -0.863097 -0.863097 -0.863097 \n",
"62 -0.959849 -1.0 -0.897195 -0.897195 -0.897195 \n",
"66 -0.958595 -1.0 1.000000 1.000000 1.000000 \n",
"71 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"73 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"77 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"82 -0.959849 -1.0 0.080010 0.080010 0.080010 \n",
"87 -0.959849 -1.0 -0.874656 -0.874656 -0.874656 \n",
"92 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"97 -0.932246 -1.0 1.000000 1.000000 1.000000 \n",
"99 -0.959849 -1.0 -0.250953 -0.250953 -0.250953 \n",
"104 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -0.606023 -1.0 1.000000 1.000000 1.000000 \n",
"112 -0.959849 -1.0 0.039555 0.039555 0.039555 \n",
"117 -0.959849 -1.0 1.000000 1.000000 1.000000 \n",
"119 -0.962359 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" LACTATE_MAX LACTATE_DIFF LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN \\\n",
"0 1.000000 -1.0 -0.835844 -0.835844 \n",
"4 1.000000 -1.0 -0.382773 -0.382773 \n",
"8 -0.828421 -1.0 -0.729239 -0.729239 \n",
"13 1.000000 -1.0 -0.702202 -0.702202 \n",
"18 1.000000 -1.0 -0.706450 -0.706450 \n",
"23 1.000000 -1.0 -0.916184 -0.916184 \n",
"28 1.000000 -1.0 -0.713789 -0.713789 \n",
"33 -0.952572 -1.0 -0.786404 -0.786404 \n",
"38 1.000000 -1.0 -0.845114 -0.845114 \n",
"43 1.000000 -1.0 -0.851294 -0.851294 \n",
"48 1.000000 -1.0 -0.867903 -0.867903 \n",
"51 0.028537 -1.0 -0.861143 -0.861143 \n",
"56 1.000000 -1.0 -0.803785 -0.803785 \n",
"60 -0.863097 -1.0 -0.770954 -0.770954 \n",
"62 -0.897195 -1.0 -0.555041 -0.555041 \n",
"66 1.000000 -1.0 -0.857088 -0.857088 \n",
"71 1.000000 -1.0 -0.835844 -0.835844 \n",
"73 1.000000 -1.0 -0.714175 -0.714175 \n",
"77 1.000000 -1.0 -0.820780 -0.820780 \n",
"82 0.080010 -1.0 -0.863654 -0.863654 \n",
"87 -0.874656 -1.0 -0.780224 -0.780224 \n",
"92 1.000000 -1.0 -0.779452 -0.779452 \n",
"97 1.000000 -1.0 -0.842024 -0.842024 \n",
"99 -0.250953 -1.0 -0.784859 -0.784859 \n",
"104 1.000000 -1.0 -0.730784 -0.730784 \n",
"109 NaN NaN NaN NaN \n",
"110 1.000000 -1.0 -0.765160 -0.765160 \n",
"112 0.039555 -1.0 -0.779838 -0.779838 \n",
"117 1.000000 -1.0 -0.631904 -0.631904 \n",
"119 1.000000 -1.0 -0.840093 -0.840093 \n",
"\n",
" LEUKOCYTES_MIN LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN \\\n",
"0 -0.835844 -0.835844 -1.0 -0.914938 \n",
"4 -0.382773 -0.382773 -1.0 -0.908714 \n",
"8 -0.729239 -0.729239 -1.0 -0.836100 \n",
"13 -0.702202 -0.702202 -1.0 -0.641079 \n",
"18 -0.706450 -0.706450 -1.0 -0.340249 \n",
"23 -0.916184 -0.916184 -1.0 -0.858921 \n",
"28 -0.713789 -0.713789 -1.0 -0.655602 \n",
"33 -0.786404 -0.786404 -1.0 -0.724066 \n",
"38 -0.845114 -0.845114 -1.0 -0.634855 \n",
"43 -0.851294 -0.851294 -1.0 -0.890041 \n",
"48 -0.867903 -0.867903 -1.0 -0.867220 \n",
"51 -0.861143 -0.861143 -1.0 -0.697095 \n",
"56 -0.803785 -0.803785 -1.0 -0.815353 \n",
"60 -0.770954 -0.770954 -1.0 -0.786307 \n",
"62 -0.555041 -0.555041 -1.0 0.080913 \n",
"66 -0.857088 -0.857088 -1.0 -0.781120 \n",
"71 -0.835844 -0.835844 -1.0 -0.827801 \n",
"73 -0.714175 -0.714175 -1.0 -0.609959 \n",
"77 -0.820780 -0.820780 -1.0 -0.697095 \n",
"82 -0.863654 -0.863654 -1.0 -0.830913 \n",
"87 -0.780224 -0.780224 -1.0 -0.578838 \n",
"92 -0.779452 -0.779452 -1.0 -0.699170 \n",
"97 -0.842024 -0.842024 -1.0 -0.925311 \n",
"99 -0.784859 -0.784859 -1.0 -0.854772 \n",
"104 -0.730784 -0.730784 -1.0 -0.558091 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.765160 -0.765160 -1.0 -0.807054 \n",
"112 -0.779838 -0.779838 -1.0 -0.703320 \n",
"117 -0.631904 -0.631904 -1.0 -0.921162 \n",
"119 -0.840093 -0.840093 -1.0 -0.826763 \n",
"\n",
" LINFOCITOS_MEAN LINFOCITOS_MIN LINFOCITOS_MAX LINFOCITOS_DIFF \\\n",
"0 -0.914938 -0.914938 -0.914938 -1.0 \n",
"4 -0.908714 -0.908714 -0.908714 -1.0 \n",
"8 -0.836100 -0.836100 -0.836100 -1.0 \n",
"13 -0.641079 -0.641079 -0.641079 -1.0 \n",
"18 -0.340249 -0.340249 -0.340249 -1.0 \n",
"23 -0.858921 -0.858921 -0.858921 -1.0 \n",
"28 -0.655602 -0.655602 -0.655602 -1.0 \n",
"33 -0.724066 -0.724066 -0.724066 -1.0 \n",
"38 -0.634855 -0.634855 -0.634855 -1.0 \n",
"43 -0.890041 -0.890041 -0.890041 -1.0 \n",
"48 -0.867220 -0.867220 -0.867220 -1.0 \n",
"51 -0.697095 -0.697095 -0.697095 -1.0 \n",
"56 -0.815353 -0.815353 -0.815353 -1.0 \n",
"60 -0.786307 -0.786307 -0.786307 -1.0 \n",
"62 0.080913 0.080913 0.080913 -1.0 \n",
"66 -0.781120 -0.781120 -0.781120 -1.0 \n",
"71 -0.827801 -0.827801 -0.827801 -1.0 \n",
"73 -0.609959 -0.609959 -0.609959 -1.0 \n",
"77 -0.697095 -0.697095 -0.697095 -1.0 \n",
"82 -0.830913 -0.830913 -0.830913 -1.0 \n",
"87 -0.578838 -0.578838 -0.578838 -1.0 \n",
"92 -0.699170 -0.699170 -0.699170 -1.0 \n",
"97 -0.925311 -0.925311 -0.925311 -1.0 \n",
"99 -0.854772 -0.854772 -0.854772 -1.0 \n",
"104 -0.558091 -0.558091 -0.558091 -1.0 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.807054 -0.807054 -0.807054 -1.0 \n",
"112 -0.703320 -0.703320 -0.703320 -1.0 \n",
"117 -0.921162 -0.921162 -0.921162 -1.0 \n",
"119 -0.826763 -0.826763 -0.826763 -1.0 \n",
"\n",
" NEUTROPHILES_MEDIAN NEUTROPHILES_MEAN NEUTROPHILES_MIN \\\n",
"0 -0.868747 -0.868747 -0.868747 \n",
"4 -0.412965 -0.412965 -0.412965 \n",
"8 -0.784714 -0.784714 -0.784714 \n",
"13 -0.812725 -0.812725 -0.812725 \n",
"18 -0.846339 -0.846339 -0.846339 \n",
"23 -0.947579 -0.947579 -0.947579 \n",
"28 -0.789916 -0.789916 -0.789916 \n",
"33 -0.851140 -0.851140 -0.851140 \n",
"38 -0.936575 -0.936575 -0.936575 \n",
"43 -0.875550 -0.875550 -0.875550 \n",
"48 -0.897959 -0.897959 -0.897959 \n",
"51 -0.927771 -0.927771 -0.927771 \n",
"56 -0.842737 -0.842737 -0.842737 \n",
"60 -0.820328 -0.820328 -0.820328 \n",
"62 -0.804722 -0.804722 -0.804722 \n",
"66 -0.907763 -0.907763 -0.907763 \n",
"71 -0.874750 -0.874750 -0.874750 \n",
"73 -0.807923 -0.807923 -0.807923 \n",
"77 -0.883553 -0.883553 -0.883553 \n",
"82 -0.907363 -0.907363 -0.907363 \n",
"87 -0.897959 -0.897959 -0.897959 \n",
"92 -0.846339 -0.846339 -0.846339 \n",
"97 -0.868747 -0.868747 -0.868747 \n",
"99 -0.816460 -0.816460 -0.816460 \n",
"104 -0.826731 -0.826731 -0.826731 \n",
"109 NaN NaN NaN \n",
"110 -0.813926 -0.813926 -0.813926 \n",
"112 -0.853141 -0.853141 -0.853141 \n",
"117 -0.649460 -0.649460 -0.649460 \n",
"119 -0.881553 -0.881553 -0.881553 \n",
"\n",
" NEUTROPHILES_MAX NEUTROPHILES_DIFF P02_ARTERIAL_MEDIAN \\\n",
"0 -0.868747 -1.0 -0.170732 \n",
"4 -0.412965 -1.0 -0.170732 \n",
"8 -0.784714 -1.0 -0.170732 \n",
"13 -0.812725 -1.0 -0.170732 \n",
"18 -0.846339 -1.0 -0.170732 \n",
"23 -0.947579 -1.0 -0.170732 \n",
"28 -0.789916 -1.0 -0.170732 \n",
"33 -0.851140 -1.0 -0.170732 \n",
"38 -0.936575 -1.0 -0.170732 \n",
"43 -0.875550 -1.0 -0.170732 \n",
"48 -0.897959 -1.0 -0.170732 \n",
"51 -0.927771 -1.0 -0.170732 \n",
"56 -0.842737 -1.0 -0.170732 \n",
"60 -0.820328 -1.0 -0.170732 \n",
"62 -0.804722 -1.0 -0.170732 \n",
"66 -0.907763 -1.0 -0.170732 \n",
"71 -0.874750 -1.0 -0.170732 \n",
"73 -0.807923 -1.0 -0.170732 \n",
"77 -0.883553 -1.0 -0.170732 \n",
"82 -0.907363 -1.0 -0.170732 \n",
"87 -0.897959 -1.0 -0.170732 \n",
"92 -0.846339 -1.0 -0.170732 \n",
"97 -0.868747 -1.0 -0.170732 \n",
"99 -0.816460 -1.0 -0.268293 \n",
"104 -0.826731 -1.0 -0.170732 \n",
"109 NaN NaN NaN \n",
"110 -0.813926 -1.0 -0.170732 \n",
"112 -0.853141 -1.0 -0.170732 \n",
"117 -0.649460 -1.0 -0.170732 \n",
"119 -0.881553 -1.0 -0.170732 \n",
"\n",
" P02_ARTERIAL_MEAN P02_ARTERIAL_MIN P02_ARTERIAL_MAX P02_ARTERIAL_DIFF \\\n",
"0 -0.170732 -0.170732 -0.170732 -1.0 \n",
"4 -0.170732 -0.170732 -0.170732 -1.0 \n",
"8 -0.170732 -0.170732 -0.170732 -1.0 \n",
"13 -0.170732 -0.170732 -0.170732 -1.0 \n",
"18 -0.170732 -0.170732 -0.170732 -1.0 \n",
"23 -0.170732 -0.170732 -0.170732 -1.0 \n",
"28 -0.170732 -0.170732 -0.170732 -1.0 \n",
"33 -0.170732 -0.170732 -0.170732 -1.0 \n",
"38 -0.170732 -0.170732 -0.170732 -1.0 \n",
"43 -0.170732 -0.170732 -0.170732 -1.0 \n",
"48 -0.170732 -0.170732 -0.170732 -1.0 \n",
"51 -0.170732 -0.170732 -0.170732 -1.0 \n",
"56 -0.170732 -0.170732 -0.170732 -1.0 \n",
"60 -0.170732 -0.170732 -0.170732 -1.0 \n",
"62 -0.170732 -0.170732 -0.170732 -1.0 \n",
"66 -0.170732 -0.170732 -0.170732 -1.0 \n",
"71 -0.170732 -0.170732 -0.170732 -1.0 \n",
"73 -0.170732 -0.170732 -0.170732 -1.0 \n",
"77 -0.170732 -0.170732 -0.170732 -1.0 \n",
"82 -0.170732 -0.170732 -0.170732 -1.0 \n",
"87 -0.170732 -0.170732 -0.170732 -1.0 \n",
"92 -0.170732 -0.170732 -0.170732 -1.0 \n",
"97 -0.170732 -0.170732 -0.170732 -1.0 \n",
"99 -0.268293 -0.268293 -0.268293 -1.0 \n",
"104 -0.170732 -0.170732 -0.170732 -1.0 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.170732 -0.170732 -0.170732 -1.0 \n",
"112 -0.170732 -0.170732 -0.170732 -1.0 \n",
"117 -0.170732 -0.170732 -0.170732 -1.0 \n",
"119 -0.170732 -0.170732 -0.170732 -1.0 \n",
"\n",
" P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN P02_VENOUS_MAX \\\n",
"0 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"4 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"8 -0.633136 -0.633136 -0.633136 -0.633136 \n",
"13 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"18 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"23 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"28 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"33 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"38 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"43 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"48 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"51 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"56 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"60 -0.940828 -0.940828 -0.940828 -0.940828 \n",
"62 -0.834320 -0.834320 -0.834320 -0.834320 \n",
"66 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"71 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"73 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"77 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"82 -0.822485 -0.822485 -0.822485 -0.822485 \n",
"87 -0.857988 -0.857988 -0.857988 -0.857988 \n",
"92 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"97 -0.514793 -0.514793 -0.514793 -0.514793 \n",
"99 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"104 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"112 -0.656805 -0.656805 -0.656805 -0.656805 \n",
"117 -0.443787 -0.443787 -0.443787 -0.443787 \n",
"119 -0.704142 -0.704142 -0.704142 -0.704142 \n",
"\n",
" P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"0 -1.0 -0.779310 -0.779310 \n",
"4 -1.0 -0.779310 -0.779310 \n",
"8 -1.0 -0.779310 -0.779310 \n",
"13 -1.0 -0.779310 -0.779310 \n",
"18 -1.0 -0.779310 -0.779310 \n",
"23 -1.0 -0.779310 -0.779310 \n",
"28 -1.0 -0.779310 -0.779310 \n",
"33 -1.0 -0.779310 -0.779310 \n",
"38 -1.0 -0.779310 -0.779310 \n",
"43 -1.0 -0.779310 -0.779310 \n",
"48 -1.0 -0.779310 -0.779310 \n",
"51 -1.0 -0.779310 -0.779310 \n",
"56 -1.0 -0.779310 -0.779310 \n",
"60 -1.0 -0.779310 -0.779310 \n",
"62 -1.0 -0.779310 -0.779310 \n",
"66 -1.0 -0.779310 -0.779310 \n",
"71 -1.0 -0.779310 -0.779310 \n",
"73 -1.0 -0.779310 -0.779310 \n",
"77 -1.0 -0.779310 -0.779310 \n",
"82 -1.0 -0.779310 -0.779310 \n",
"87 -1.0 -0.779310 -0.779310 \n",
"92 -1.0 -0.779310 -0.779310 \n",
"97 -1.0 -0.779310 -0.779310 \n",
"99 -1.0 -0.825287 -0.825287 \n",
"104 -1.0 -0.779310 -0.779310 \n",
"109 NaN NaN NaN \n",
"110 -1.0 -0.779310 -0.779310 \n",
"112 -1.0 -0.779310 -0.779310 \n",
"117 -1.0 -0.779310 -0.779310 \n",
"119 -1.0 -0.779310 -0.779310 \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"0 -0.779310 -0.779310 -1.0 \n",
"4 -0.779310 -0.779310 -1.0 \n",
"8 -0.779310 -0.779310 -1.0 \n",
"13 -0.779310 -0.779310 -1.0 \n",
"18 -0.779310 -0.779310 -1.0 \n",
"23 -0.779310 -0.779310 -1.0 \n",
"28 -0.779310 -0.779310 -1.0 \n",
"33 -0.779310 -0.779310 -1.0 \n",
"38 -0.779310 -0.779310 -1.0 \n",
"43 -0.779310 -0.779310 -1.0 \n",
"48 -0.779310 -0.779310 -1.0 \n",
"51 -0.779310 -0.779310 -1.0 \n",
"56 -0.779310 -0.779310 -1.0 \n",
"60 -0.779310 -0.779310 -1.0 \n",
"62 -0.779310 -0.779310 -1.0 \n",
"66 -0.779310 -0.779310 -1.0 \n",
"71 -0.779310 -0.779310 -1.0 \n",
"73 -0.779310 -0.779310 -1.0 \n",
"77 -0.779310 -0.779310 -1.0 \n",
"82 -0.779310 -0.779310 -1.0 \n",
"87 -0.779310 -0.779310 -1.0 \n",
"92 -0.779310 -0.779310 -1.0 \n",
"97 -0.779310 -0.779310 -1.0 \n",
"99 -0.825287 -0.825287 -1.0 \n",
"104 -0.779310 -0.779310 -1.0 \n",
"109 NaN NaN NaN \n",
"110 -0.779310 -0.779310 -1.0 \n",
"112 -0.779310 -0.779310 -1.0 \n",
"117 -0.779310 -0.779310 -1.0 \n",
"119 -0.779310 -0.779310 -1.0 \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"0 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"4 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"8 -0.779141 -0.779141 -0.779141 -0.779141 \n",
"13 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"18 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"23 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"28 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"33 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"38 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"43 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"48 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"51 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"56 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"60 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"62 -0.889571 -0.889571 -0.889571 -0.889571 \n",
"66 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"71 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"73 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"77 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"82 -0.797546 -0.797546 -0.797546 -0.797546 \n",
"87 -0.546012 -0.546012 -0.546012 -0.546012 \n",
"92 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"97 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"99 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"104 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"112 -0.797546 -0.797546 -0.797546 -0.797546 \n",
"117 -0.865031 -0.865031 -0.865031 -0.865031 \n",
"119 -0.754601 -0.754601 -0.754601 -0.754601 \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF \\\n",
"0 -1.0 -0.875236 -0.875236 -0.875236 -0.875236 -1.0 \n",
"4 -1.0 -0.939887 -0.939887 -0.939887 -0.939887 -1.0 \n",
"8 -1.0 -0.503592 -0.503592 -0.503592 -0.503592 -1.0 \n",
"13 -1.0 -0.990926 -0.990926 -0.990926 -0.990926 -1.0 \n",
"18 -1.0 -0.997732 -0.997732 -0.997732 -0.997732 -1.0 \n",
"23 -1.0 -0.750095 -0.750095 -0.750095 -0.750095 -1.0 \n",
"28 -1.0 -0.999244 -0.999244 -0.999244 -0.999244 -1.0 \n",
"33 -1.0 -0.798110 -0.798110 -0.798110 -0.798110 -1.0 \n",
"38 -1.0 -0.994140 -0.994140 -0.994140 -0.994140 -1.0 \n",
"43 -1.0 -0.462004 -0.462004 -0.462004 -0.462004 -1.0 \n",
"48 -1.0 -0.679017 -0.679017 -0.679017 -0.679017 -1.0 \n",
"51 -1.0 -0.989603 -0.989603 -0.989603 -0.989603 -1.0 \n",
"56 -1.0 -0.688847 -0.688847 -0.688847 -0.688847 -1.0 \n",
"60 -1.0 -0.857845 -0.857845 -0.857845 -0.857845 -1.0 \n",
"62 -1.0 -0.388658 -0.388658 -0.388658 -0.388658 -1.0 \n",
"66 -1.0 -0.986011 -0.986011 -0.986011 -0.986011 -1.0 \n",
"71 -1.0 -0.513422 -0.513422 -0.513422 -0.513422 -1.0 \n",
"73 -1.0 -0.999244 -0.999244 -0.999244 -0.999244 -1.0 \n",
"77 -1.0 -0.941399 -0.941399 -0.941399 -0.941399 -1.0 \n",
"82 -1.0 -0.842911 -0.842911 -0.842911 -0.842911 -1.0 \n",
"87 -1.0 -0.997732 -0.997732 -0.997732 -0.997732 -1.0 \n",
"92 -1.0 -0.613233 -0.613233 -0.613233 -0.613233 -1.0 \n",
"97 -1.0 -0.958412 -0.958412 -0.958412 -0.958412 -1.0 \n",
"99 -1.0 -0.730309 -0.730309 -0.730309 -0.730309 -1.0 \n",
"104 -1.0 -0.874858 -0.874858 -0.874858 -0.874858 -1.0 \n",
"109 NaN NaN NaN NaN NaN NaN \n",
"110 -1.0 -0.734594 -0.734594 -0.734594 -0.734594 -1.0 \n",
"112 -1.0 -0.988658 -0.988658 -0.988658 -0.988658 -1.0 \n",
"117 -1.0 -0.116068 -0.116068 -0.116068 -0.116068 -1.0 \n",
"119 -1.0 -0.866163 -0.866163 -0.866163 -0.866163 -1.0 \n",
"\n",
" PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN PH_ARTERIAL_MAX \\\n",
"0 0.234043 0.234043 0.234043 0.234043 \n",
"4 0.234043 0.234043 0.234043 0.234043 \n",
"8 0.234043 0.234043 0.234043 0.234043 \n",
"13 0.234043 0.234043 0.234043 0.234043 \n",
"18 0.234043 0.234043 0.234043 0.234043 \n",
"23 0.234043 0.234043 0.234043 0.234043 \n",
"28 0.234043 0.234043 0.234043 0.234043 \n",
"33 0.234043 0.234043 0.234043 0.234043 \n",
"38 0.234043 0.234043 0.234043 0.234043 \n",
"43 0.234043 0.234043 0.234043 0.234043 \n",
"48 0.234043 0.234043 0.234043 0.234043 \n",
"51 0.234043 0.234043 0.234043 0.234043 \n",
"56 0.234043 0.234043 0.234043 0.234043 \n",
"60 0.234043 0.234043 0.234043 0.234043 \n",
"62 0.234043 0.234043 0.234043 0.234043 \n",
"66 0.234043 0.234043 0.234043 0.234043 \n",
"71 0.234043 0.234043 0.234043 0.234043 \n",
"73 0.234043 0.234043 0.234043 0.234043 \n",
"77 0.234043 0.234043 0.234043 0.234043 \n",
"82 0.234043 0.234043 0.234043 0.234043 \n",
"87 0.234043 0.234043 0.234043 0.234043 \n",
"92 0.234043 0.234043 0.234043 0.234043 \n",
"97 0.234043 0.234043 0.234043 0.234043 \n",
"99 0.375887 0.375887 0.375887 0.375887 \n",
"104 0.234043 0.234043 0.234043 0.234043 \n",
"109 NaN NaN NaN NaN \n",
"110 0.234043 0.234043 0.234043 0.234043 \n",
"112 0.234043 0.234043 0.234043 0.234043 \n",
"117 0.234043 0.234043 0.234043 0.234043 \n",
"119 0.234043 0.234043 0.234043 0.234043 \n",
"\n",
" PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN \\\n",
"0 -1.0 0.363636 0.363636 0.363636 \n",
"4 -1.0 0.363636 0.363636 0.363636 \n",
"8 -1.0 0.363636 0.363636 0.363636 \n",
"13 -1.0 0.363636 0.363636 0.363636 \n",
"18 -1.0 0.363636 0.363636 0.363636 \n",
"23 -1.0 0.363636 0.363636 0.363636 \n",
"28 -1.0 0.363636 0.363636 0.363636 \n",
"33 -1.0 0.363636 0.363636 0.363636 \n",
"38 -1.0 0.363636 0.363636 0.363636 \n",
"43 -1.0 0.363636 0.363636 0.363636 \n",
"48 -1.0 0.363636 0.363636 0.363636 \n",
"51 -1.0 0.363636 0.363636 0.363636 \n",
"56 -1.0 0.363636 0.363636 0.363636 \n",
"60 -1.0 0.393939 0.393939 0.393939 \n",
"62 -1.0 0.515152 0.515152 0.515152 \n",
"66 -1.0 0.363636 0.363636 0.363636 \n",
"71 -1.0 0.363636 0.363636 0.363636 \n",
"73 -1.0 0.363636 0.363636 0.363636 \n",
"77 -1.0 0.363636 0.363636 0.363636 \n",
"82 -1.0 0.469697 0.469697 0.469697 \n",
"87 -1.0 0.151515 0.151515 0.151515 \n",
"92 -1.0 0.363636 0.363636 0.363636 \n",
"97 -1.0 0.848485 0.848485 0.848485 \n",
"99 -1.0 0.363636 0.363636 0.363636 \n",
"104 -1.0 0.363636 0.363636 0.363636 \n",
"109 NaN NaN NaN NaN \n",
"110 -1.0 0.363636 0.363636 0.363636 \n",
"112 -1.0 0.424242 0.424242 0.424242 \n",
"117 -1.0 0.363636 0.363636 0.363636 \n",
"119 -1.0 0.363636 0.363636 0.363636 \n",
"\n",
" PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN PLATELETS_MEAN \\\n",
"0 0.363636 -1.0 -0.540721 -0.540721 \n",
"4 0.363636 -1.0 -0.399199 -0.399199 \n",
"8 0.363636 -1.0 -0.564753 -0.564753 \n",
"13 0.363636 -1.0 -0.457944 -0.457944 \n",
"18 0.363636 -1.0 -0.292390 -0.292390 \n",
"23 0.363636 -1.0 -0.636849 -0.636849 \n",
"28 0.363636 -1.0 -0.564753 -0.564753 \n",
"33 0.363636 -1.0 -0.522029 -0.522029 \n",
"38 0.363636 -1.0 -0.751669 -0.751669 \n",
"43 0.363636 -1.0 -0.751669 -0.751669 \n",
"48 0.363636 -1.0 -0.516689 -0.516689 \n",
"51 0.363636 -1.0 -0.506008 -0.506008 \n",
"56 0.363636 -1.0 -0.527370 -0.527370 \n",
"60 0.393939 -1.0 -0.428571 -0.428571 \n",
"62 0.515152 -1.0 -0.674232 -0.674232 \n",
"66 0.363636 -1.0 -0.619493 -0.619493 \n",
"71 0.363636 -1.0 -0.471295 -0.471295 \n",
"73 0.363636 -1.0 -0.732977 -0.732977 \n",
"77 0.363636 -1.0 -0.572764 -0.572764 \n",
"82 0.469697 -1.0 -0.723632 -0.723632 \n",
"87 0.151515 -1.0 -0.468625 -0.468625 \n",
"92 0.363636 -1.0 -0.287049 -0.287049 \n",
"97 0.848485 -1.0 -0.754339 -0.754339 \n",
"99 0.363636 -1.0 -0.243436 -0.243436 \n",
"104 0.363636 -1.0 -0.455274 -0.455274 \n",
"109 NaN NaN NaN NaN \n",
"110 0.363636 -1.0 -0.671562 -0.671562 \n",
"112 0.424242 -1.0 -0.546061 -0.546061 \n",
"117 0.363636 -1.0 -0.652870 -0.652870 \n",
"119 0.363636 -1.0 -0.455274 -0.455274 \n",
"\n",
" PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF POTASSIUM_MEDIAN \\\n",
"0 -0.540721 -0.540721 -1.0 -0.518519 \n",
"4 -0.399199 -0.399199 -1.0 -0.703704 \n",
"8 -0.564753 -0.564753 -1.0 -0.777778 \n",
"13 -0.457944 -0.457944 -1.0 -0.592593 \n",
"18 -0.292390 -0.292390 -1.0 -0.666667 \n",
"23 -0.636849 -0.636849 -1.0 -0.962963 \n",
"28 -0.564753 -0.564753 -1.0 -0.703704 \n",
"33 -0.522029 -0.522029 -1.0 -0.777778 \n",
"38 -0.751669 -0.751669 -1.0 -0.444444 \n",
"43 -0.751669 -0.751669 -1.0 -0.629630 \n",
"48 -0.516689 -0.516689 -1.0 -0.666667 \n",
"51 -0.506008 -0.506008 -1.0 -0.592593 \n",
"56 -0.527370 -0.527370 -1.0 -0.666667 \n",
"60 -0.428571 -0.428571 -1.0 -0.703704 \n",
"62 -0.674232 -0.674232 -1.0 -0.481481 \n",
"66 -0.619493 -0.619493 -1.0 -0.481481 \n",
"71 -0.471295 -0.471295 -1.0 -0.481481 \n",
"73 -0.732977 -0.732977 -1.0 -0.518519 \n",
"77 -0.572764 -0.572764 -1.0 -0.629630 \n",
"82 -0.723632 -0.723632 -1.0 -0.740741 \n",
"87 -0.468625 -0.468625 -1.0 -0.518519 \n",
"92 -0.287049 -0.287049 -1.0 -0.481481 \n",
"97 -0.754339 -0.754339 -1.0 -0.777778 \n",
"99 -0.243436 -0.243436 -1.0 -0.530864 \n",
"104 -0.455274 -0.455274 -1.0 -0.592593 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.671562 -0.671562 -1.0 -0.296296 \n",
"112 -0.546061 -0.546061 -1.0 -0.574074 \n",
"117 -0.652870 -0.652870 -1.0 -0.407407 \n",
"119 -0.455274 -0.455274 -1.0 -0.611111 \n",
"\n",
" POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF \\\n",
"0 -0.518519 -0.518519 -0.518519 -1.0 \n",
"4 -0.703704 -0.703704 -0.703704 -1.0 \n",
"8 -0.777778 -0.777778 -0.777778 -1.0 \n",
"13 -0.592593 -0.592593 -0.592593 -1.0 \n",
"18 -0.666667 -0.666667 -0.666667 -1.0 \n",
"23 -0.962963 -0.962963 -0.962963 -1.0 \n",
"28 -0.703704 -0.703704 -0.703704 -1.0 \n",
"33 -0.777778 -0.777778 -0.777778 -1.0 \n",
"38 -0.444444 -0.444444 -0.444444 -1.0 \n",
"43 -0.629630 -0.629630 -0.629630 -1.0 \n",
"48 -0.666667 -0.666667 -0.666667 -1.0 \n",
"51 -0.592593 -0.592593 -0.592593 -1.0 \n",
"56 -0.666667 -0.666667 -0.666667 -1.0 \n",
"60 -0.703704 -0.703704 -0.703704 -1.0 \n",
"62 -0.481481 -0.481481 -0.481481 -1.0 \n",
"66 -0.481481 -0.481481 -0.481481 -1.0 \n",
"71 -0.481481 -0.481481 -0.481481 -1.0 \n",
"73 -0.518519 -0.518519 -0.518519 -1.0 \n",
"77 -0.629630 -0.629630 -0.629630 -1.0 \n",
"82 -0.740741 -0.740741 -0.740741 -1.0 \n",
"87 -0.518519 -0.518519 -0.518519 -1.0 \n",
"92 -0.481481 -0.481481 -0.481481 -1.0 \n",
"97 -0.777778 -0.777778 -0.777778 -1.0 \n",
"99 -0.530864 -0.530864 -0.530864 -1.0 \n",
"104 -0.592593 -0.592593 -0.592593 -1.0 \n",
"109 NaN NaN NaN NaN \n",
"110 -0.296296 -0.296296 -0.296296 -1.0 \n",
"112 -0.574074 -0.574074 -0.574074 -1.0 \n",
"117 -0.407407 -0.407407 -0.407407 -1.0 \n",
"119 -0.611111 -0.611111 -0.611111 -1.0 \n",
"\n",
" SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN SAT02_ARTERIAL_MIN \\\n",
"0 0.939394 0.939394 0.939394 \n",
"4 0.939394 0.939394 0.939394 \n",
"8 0.939394 0.939394 0.939394 \n",
"13 0.939394 0.939394 0.939394 \n",
"18 0.939394 0.939394 0.939394 \n",
"23 0.939394 0.939394 0.939394 \n",
"28 0.939394 0.939394 0.939394 \n",
"33 0.939394 0.939394 0.939394 \n",
"38 0.939394 0.939394 0.939394 \n",
"43 0.939394 0.939394 0.939394 \n",
"48 0.939394 0.939394 0.939394 \n",
"51 0.939394 0.939394 0.939394 \n",
"56 0.939394 0.939394 0.939394 \n",
"60 0.939394 0.939394 0.939394 \n",
"62 0.939394 0.939394 0.939394 \n",
"66 0.939394 0.939394 0.939394 \n",
"71 0.939394 0.939394 0.939394 \n",
"73 0.939394 0.939394 0.939394 \n",
"77 0.939394 0.939394 0.939394 \n",
"82 0.939394 0.939394 0.939394 \n",
"87 0.939394 0.939394 0.939394 \n",
"92 0.939394 0.939394 0.939394 \n",
"97 0.939394 0.939394 0.939394 \n",
"99 0.888889 0.888889 0.888889 \n",
"104 0.939394 0.939394 0.939394 \n",
"109 NaN NaN NaN \n",
"110 0.939394 0.939394 0.939394 \n",
"112 0.939394 0.939394 0.939394 \n",
"117 0.939394 0.939394 0.939394 \n",
"119 0.939394 0.939394 0.939394 \n",
"\n",
" SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN \\\n",
"0 0.939394 -1.0 0.345679 \n",
"4 0.939394 -1.0 0.345679 \n",
"8 0.939394 -1.0 0.580247 \n",
"13 0.939394 -1.0 0.345679 \n",
"18 0.939394 -1.0 0.345679 \n",
"23 0.939394 -1.0 0.345679 \n",
"28 0.939394 -1.0 0.345679 \n",
"33 0.939394 -1.0 0.345679 \n",
"38 0.939394 -1.0 0.345679 \n",
"43 0.939394 -1.0 0.345679 \n",
"48 0.939394 -1.0 0.345679 \n",
"51 0.939394 -1.0 0.345679 \n",
"56 0.939394 -1.0 0.345679 \n",
"60 0.939394 -1.0 -0.728395 \n",
"62 0.939394 -1.0 -0.160494 \n",
"66 0.939394 -1.0 0.345679 \n",
"71 0.939394 -1.0 0.345679 \n",
"73 0.939394 -1.0 0.345679 \n",
"77 0.939394 -1.0 0.345679 \n",
"82 0.939394 -1.0 -0.166667 \n",
"87 0.939394 -1.0 -0.407407 \n",
"92 0.939394 -1.0 0.345679 \n",
"97 0.939394 -1.0 0.827160 \n",
"99 0.888889 -1.0 0.345679 \n",
"104 0.939394 -1.0 0.345679 \n",
"109 NaN NaN NaN \n",
"110 0.939394 -1.0 0.345679 \n",
"112 0.939394 -1.0 0.475309 \n",
"117 0.939394 -1.0 0.802469 \n",
"119 0.939394 -1.0 0.345679 \n",
"\n",
" SAT02_VENOUS_MEAN SAT02_VENOUS_MIN SAT02_VENOUS_MAX SAT02_VENOUS_DIFF \\\n",
"0 0.345679 0.345679 0.345679 -1.0 \n",
"4 0.345679 0.345679 0.345679 -1.0 \n",
"8 0.580247 0.580247 0.580247 -1.0 \n",
"13 0.345679 0.345679 0.345679 -1.0 \n",
"18 0.345679 0.345679 0.345679 -1.0 \n",
"23 0.345679 0.345679 0.345679 -1.0 \n",
"28 0.345679 0.345679 0.345679 -1.0 \n",
"33 0.345679 0.345679 0.345679 -1.0 \n",
"38 0.345679 0.345679 0.345679 -1.0 \n",
"43 0.345679 0.345679 0.345679 -1.0 \n",
"48 0.345679 0.345679 0.345679 -1.0 \n",
"51 0.345679 0.345679 0.345679 -1.0 \n",
"56 0.345679 0.345679 0.345679 -1.0 \n",
"60 -0.728395 -0.728395 -0.728395 -1.0 \n",
"62 -0.160494 -0.160494 -0.160494 -1.0 \n",
"66 0.345679 0.345679 0.345679 -1.0 \n",
"71 0.345679 0.345679 0.345679 -1.0 \n",
"73 0.345679 0.345679 0.345679 -1.0 \n",
"77 0.345679 0.345679 0.345679 -1.0 \n",
"82 -0.166667 -0.166667 -0.166667 -1.0 \n",
"87 -0.407407 -0.407407 -0.407407 -1.0 \n",
"92 0.345679 0.345679 0.345679 -1.0 \n",
"97 0.827160 0.827160 0.827160 -1.0 \n",
"99 0.345679 0.345679 0.345679 -1.0 \n",
"104 0.345679 0.345679 0.345679 -1.0 \n",
"109 NaN NaN NaN NaN \n",
"110 0.345679 0.345679 0.345679 -1.0 \n",
"112 0.475309 0.475309 0.475309 -1.0 \n",
"117 0.802469 0.802469 0.802469 -1.0 \n",
"119 0.345679 0.345679 0.345679 -1.0 \n",
"\n",
" SODIUM_MEDIAN SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF \\\n",
"0 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"4 0.085714 0.085714 0.085714 0.085714 -1.0 \n",
"8 0.200000 0.200000 0.200000 0.200000 -1.0 \n",
"13 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"18 0.085714 0.085714 0.085714 0.085714 -1.0 \n",
"23 -0.257143 -0.257143 -0.257143 -0.257143 -1.0 \n",
"28 -0.142857 -0.142857 -0.142857 -0.142857 -1.0 \n",
"33 -0.600000 -0.600000 -0.600000 -0.600000 -1.0 \n",
"38 0.161905 0.161905 0.161905 0.161905 -1.0 \n",
"43 0.028571 0.028571 0.028571 0.028571 -1.0 \n",
"48 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"51 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"56 -0.257143 -0.257143 -0.257143 -0.257143 -1.0 \n",
"60 -0.542857 -0.542857 -0.542857 -0.542857 -1.0 \n",
"62 -0.314286 -0.314286 -0.314286 -0.314286 -1.0 \n",
"66 -0.142857 -0.142857 -0.142857 -0.142857 -1.0 \n",
"71 -0.085714 -0.085714 -0.085714 -0.085714 -1.0 \n",
"73 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"77 -0.085714 -0.085714 -0.085714 -0.085714 -1.0 \n",
"82 -0.257143 -0.257143 -0.257143 -0.257143 -1.0 \n",
"87 0.142857 0.142857 0.142857 0.142857 -1.0 \n",
"92 0.066667 0.066667 0.066667 0.066667 -1.0 \n",
"97 0.028571 0.028571 0.028571 0.028571 -1.0 \n",
"99 -0.187302 -0.187302 -0.187302 -0.187302 -1.0 \n",
"104 -0.028571 -0.028571 -0.028571 -0.028571 -1.0 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -0.200000 -0.200000 -0.200000 -0.200000 -1.0 \n",
"112 0.085714 0.085714 0.085714 0.085714 -1.0 \n",
"117 -0.314286 -0.314286 -0.314286 -0.314286 -1.0 \n",
"119 -0.171429 -0.171429 -0.171429 -0.171429 -1.0 \n",
"\n",
" TGO_MEDIAN TGO_MEAN TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN \\\n",
"0 -0.997201 -0.997201 -0.997201 -0.997201 -1.0 -0.990854 -0.990854 \n",
"4 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"8 -0.989549 -0.989549 -0.989549 -0.989549 -1.0 -0.956555 -0.956555 \n",
"13 -0.998507 -0.998507 -0.998507 -0.998507 -1.0 -0.991235 -0.991235 \n",
"18 -0.997947 -0.997947 -0.997947 -0.997947 -1.0 -0.988948 -0.988948 \n",
"23 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"28 -0.995708 -0.995708 -0.995708 -0.995708 -1.0 -0.979040 -0.979040 \n",
"33 -0.998134 -0.998134 -0.998134 -0.998134 -1.0 -0.997332 -0.997332 \n",
"38 -0.994261 -0.994261 -0.994261 -0.994261 -1.0 -0.984947 -0.984947 \n",
"43 -0.997014 -0.997014 -0.997014 -0.997014 -1.0 -0.996189 -0.996189 \n",
"48 -0.996827 -0.996827 -0.996827 -0.996827 -1.0 -0.999619 -0.999619 \n",
"51 -0.997481 -0.997481 -0.997481 -0.997481 -1.0 -0.996570 -0.996570 \n",
"56 -0.994775 -0.994775 -0.994775 -0.994775 -1.0 -0.984756 -0.984756 \n",
"60 -0.996268 -0.996268 -0.996268 -0.996268 -1.0 -0.994665 -0.994665 \n",
"62 -0.991789 -0.991789 -0.991789 -0.991789 -1.0 -0.977515 -0.977515 \n",
"66 -0.995894 -0.995894 -0.995894 -0.995894 -1.0 -0.990663 -0.990663 \n",
"71 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"73 -0.998880 -0.998880 -0.998880 -0.998880 -1.0 -0.996570 -0.996570 \n",
"77 -0.997761 -0.997761 -0.997761 -0.997761 -1.0 -0.993521 -0.993521 \n",
"82 -0.986517 -0.986517 -0.986517 -0.986517 -1.0 -0.970274 -0.970274 \n",
"87 -0.997014 -0.997014 -0.997014 -0.997014 -1.0 -0.991235 -0.991235 \n",
"92 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"97 -0.995521 -0.995521 -0.995521 -0.995521 -1.0 -0.991235 -0.991235 \n",
"99 -0.994930 -0.994930 -0.994930 -0.994930 -1.0 -0.986916 -0.986916 \n",
"104 -0.995428 -0.995428 -0.995428 -0.995428 -1.0 -0.986662 -0.986662 \n",
"109 NaN NaN NaN NaN NaN NaN NaN \n",
"110 -0.988430 -0.988430 -0.988430 -0.988430 -1.0 -0.975991 -0.975991 \n",
"112 -0.997947 -0.997947 -0.997947 -0.997947 -1.0 -0.996761 -0.996761 \n",
"117 -0.964542 -0.964542 -0.964542 -0.964542 -1.0 -0.946265 -0.946265 \n",
"119 -0.997201 -0.997201 -0.997201 -0.997201 -1.0 -0.992759 -0.992759 \n",
"\n",
" TGP_MIN TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"0 -0.990854 -0.990854 -1.0 -0.825613 -0.825613 -0.825613 -0.825613 \n",
"4 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"8 -0.956555 -0.956555 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"13 -0.991235 -0.991235 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"18 -0.988948 -0.988948 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"23 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"28 -0.979040 -0.979040 -1.0 -0.934605 -0.934605 -0.934605 -0.934605 \n",
"33 -0.997332 -0.997332 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"38 -0.984947 -0.984947 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"43 -0.996189 -0.996189 -1.0 -0.847411 -0.847411 -0.847411 -0.847411 \n",
"48 -0.999619 -0.999619 -1.0 -0.803815 -0.803815 -0.803815 -0.803815 \n",
"51 -0.996570 -0.996570 -1.0 -0.833398 -0.833398 -0.833398 -0.833398 \n",
"56 -0.984756 -0.984756 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"60 -0.994665 -0.994665 -1.0 -0.891008 -0.891008 -0.891008 -0.891008 \n",
"62 -0.977515 -0.977515 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"66 -0.990663 -0.990663 -1.0 -0.849747 -0.849747 -0.849747 -0.849747 \n",
"71 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"73 -0.996570 -0.996570 -1.0 -0.874659 -0.874659 -0.874659 -0.874659 \n",
"77 -0.993521 -0.993521 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"82 -0.970274 -0.970274 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"87 -0.991235 -0.991235 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"92 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"97 -0.991235 -0.991235 -1.0 -0.891008 -0.891008 -0.891008 -0.891008 \n",
"99 -0.986916 -0.986916 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"104 -0.986662 -0.986662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"109 NaN NaN NaN NaN NaN NaN NaN \n",
"110 -0.975991 -0.975991 -1.0 -0.711172 -0.711172 -0.711172 -0.711172 \n",
"112 -0.996761 -0.996761 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"117 -0.946265 -0.946265 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"119 -0.992759 -0.992759 -1.0 -0.830673 -0.830673 -0.830673 -0.830673 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"0 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"4 -1.0 -0.836145 -0.836145 -0.836145 -0.836145 -1.0 \n",
"8 -1.0 -0.937349 -0.937349 -0.937349 -0.937349 -1.0 \n",
"13 -1.0 -0.903614 -0.903614 -0.903614 -0.903614 -1.0 \n",
"18 -1.0 -0.884337 -0.884337 -0.884337 -0.884337 -1.0 \n",
"23 -1.0 -0.951807 -0.951807 -0.951807 -0.951807 -1.0 \n",
"28 -1.0 -0.865060 -0.865060 -0.865060 -0.865060 -1.0 \n",
"33 -1.0 -0.874699 -0.874699 -0.874699 -0.874699 -1.0 \n",
"38 -1.0 -0.886747 -0.886747 -0.886747 -0.886747 -1.0 \n",
"43 -1.0 -0.744578 -0.744578 -0.744578 -0.744578 -1.0 \n",
"48 -1.0 -0.845783 -0.845783 -0.845783 -0.845783 -1.0 \n",
"51 -1.0 -0.934940 -0.934940 -0.934940 -0.934940 -1.0 \n",
"56 -1.0 -0.754217 -0.754217 -0.754217 -0.754217 -1.0 \n",
"60 -1.0 -0.725301 -0.725301 -0.725301 -0.725301 -1.0 \n",
"62 -1.0 -0.792771 -0.792771 -0.792771 -0.792771 -1.0 \n",
"66 -1.0 -0.848193 -0.848193 -0.848193 -0.848193 -1.0 \n",
"71 -1.0 -0.898795 -0.898795 -0.898795 -0.898795 -1.0 \n",
"73 -1.0 -0.778313 -0.778313 -0.778313 -0.778313 -1.0 \n",
"77 -1.0 -0.913253 -0.913253 -0.913253 -0.913253 -1.0 \n",
"82 -1.0 -0.855422 -0.855422 -0.855422 -0.855422 -1.0 \n",
"87 -1.0 -0.812048 -0.812048 -0.812048 -0.812048 -1.0 \n",
"92 -1.0 -0.922892 -0.922892 -0.922892 -0.922892 -1.0 \n",
"97 -1.0 -0.879518 -0.879518 -0.879518 -0.879518 -1.0 \n",
"99 -1.0 -0.855422 -0.855422 -0.855422 -0.855422 -1.0 \n",
"104 -1.0 -0.913253 -0.913253 -0.913253 -0.913253 -1.0 \n",
"109 NaN NaN NaN NaN NaN NaN \n",
"110 -1.0 -0.821687 -0.821687 -0.821687 -0.821687 -1.0 \n",
"112 -1.0 -0.881928 -0.881928 -0.881928 -0.881928 -1.0 \n",
"117 -1.0 -0.556627 -0.556627 -0.556627 -0.556627 -1.0 \n",
"119 -1.0 -0.925301 -0.925301 -0.925301 -0.925301 -1.0 \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"0 -0.994912 -0.994912 -0.994912 -0.994912 -1.0 \n",
"4 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"8 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"13 -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"18 -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"23 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"28 -0.974174 -0.974174 -0.974174 -0.974174 -1.0 \n",
"33 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"38 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"43 -0.971322 -0.971322 -0.971322 -0.971322 -1.0 \n",
"48 -0.977720 -0.977720 -0.977720 -0.977720 -1.0 \n",
"51 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"56 -0.956289 -0.956289 -0.956289 -0.956289 -1.0 \n",
"60 -0.992214 -0.992214 -0.992214 -0.992214 -1.0 \n",
"62 -0.953051 -0.953051 -0.953051 -0.953051 -1.0 \n",
"66 -0.961223 -0.961223 -0.961223 -0.961223 -1.0 \n",
"71 -0.993216 -0.993216 -0.993216 -0.993216 -1.0 \n",
"73 -0.998073 -0.998073 -0.998073 -0.998073 -1.0 \n",
"77 -0.994912 -0.994912 -0.994912 -0.994912 -1.0 \n",
"82 -0.963767 -0.963767 -0.963767 -0.963767 -1.0 \n",
"87 -0.978645 -0.978645 -0.978645 -0.978645 -1.0 \n",
"92 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"97 -0.981960 -0.981960 -0.981960 -0.981960 -1.0 \n",
"99 -0.971810 -0.971810 -0.971810 -0.971810 -1.0 \n",
"104 -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"109 NaN NaN NaN NaN NaN \n",
"110 -0.990363 -0.990363 -0.990363 -0.990363 -1.0 \n",
"112 -0.982500 -0.982500 -0.982500 -0.982500 -1.0 \n",
"117 -0.946036 -0.946036 -0.946036 -0.946036 -1.0 \n",
"119 -0.986740 -0.986740 -0.986740 -0.986740 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN \\\n",
"0 0.086420 -0.230769 \n",
"4 -0.551183 -0.757158 \n",
"8 -0.132620 -0.484650 \n",
"13 0.236838 -0.101680 \n",
"18 -0.078189 -0.494587 \n",
"23 -0.144033 -0.266667 \n",
"28 -0.028472 -0.421855 \n",
"33 -0.185185 -0.630769 \n",
"38 0.050505 -0.114685 \n",
"43 0.259259 -0.292308 \n",
"48 0.037037 -0.246154 \n",
"51 -0.254870 -0.815043 \n",
"56 -0.358025 -0.461538 \n",
"60 -0.234568 -0.553846 \n",
"62 -0.131687 -0.635897 \n",
"66 0.118166 -0.404212 \n",
"71 NaN NaN \n",
"73 -0.234568 0.123077 \n",
"77 -0.082703 -0.613317 \n",
"82 -0.323045 -0.691453 \n",
"87 0.004703 -0.158425 \n",
"92 -0.047512 -0.464569 \n",
"97 NaN NaN \n",
"99 -0.107248 -0.378179 \n",
"104 0.011767 -0.514183 \n",
"109 NaN NaN \n",
"110 -0.407407 -0.615385 \n",
"112 -0.120424 -0.493199 \n",
"117 NaN NaN \n",
"119 -0.090698 -0.457045 \n",
"\n",
" HEART_RATE_MEAN RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN \\\n",
"0 -0.283019 -0.593220 -2.857143e-01 \n",
"4 -0.005241 -0.683145 3.571429e-01 \n",
"8 -0.448553 -0.506215 -1.197619e-01 \n",
"13 0.144491 -0.539451 1.891420e-01 \n",
"18 -0.454228 -0.495292 -1.375661e-01 \n",
"23 -0.284591 -0.550847 2.678571e-01 \n",
"28 -0.492833 -0.484956 -1.600914e-01 \n",
"33 -0.320755 -0.593220 4.642857e-01 \n",
"38 -0.461407 -0.534669 -1.055195e-01 \n",
"43 -0.754717 -0.661017 1.071429e-01 \n",
"48 -0.415094 -0.389831 7.857143e-01 \n",
"51 -0.265269 -0.537979 -1.087302e-01 \n",
"56 -0.712264 -0.576271 -8.035714e-02 \n",
"60 -0.641509 -0.389831 -1.785714e-01 \n",
"62 -0.182390 -0.367232 9.020562e-16 \n",
"66 -0.360288 -0.569814 4.464286e-03 \n",
"71 NaN NaN NaN \n",
"73 -0.811321 -0.186441 -1.071429e-01 \n",
"77 -0.367722 -0.556042 1.575461e-01 \n",
"82 -0.341195 -0.500942 2.132937e-01 \n",
"87 -0.289982 -0.556901 -4.634354e-02 \n",
"92 -0.183819 -0.534669 1.664863e-01 \n",
"97 -0.358491 NaN 2.142857e-01 \n",
"99 -0.443974 -0.651332 1.327745e-01 \n",
"104 -0.204746 -0.546610 2.313988e-01 \n",
"109 NaN NaN NaN \n",
"110 -0.660377 -0.525424 1.785714e-01 \n",
"112 -0.565949 -0.529986 -2.043371e-01 \n",
"117 NaN NaN NaN \n",
"119 -0.113021 -0.501594 1.252526e-01 \n",
"\n",
" OXYGEN_SATURATION_MEAN BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"0 0.736842 0.086420 \n",
"4 0.867507 -0.555556 \n",
"8 0.652398 -0.102881 \n",
"13 0.852390 0.251029 \n",
"18 0.835283 -0.090535 \n",
"23 0.377193 -0.160494 \n",
"28 0.812627 -0.020576 \n",
"33 0.738346 -0.185185 \n",
"38 0.784689 0.086420 \n",
"43 0.789474 0.259259 \n",
"48 0.684211 0.037037 \n",
"51 0.874074 -0.251029 \n",
"56 0.684211 -0.358025 \n",
"60 0.789474 -0.234568 \n",
"62 0.605263 -0.111111 \n",
"66 0.748120 0.086420 \n",
"71 NaN NaN \n",
"73 0.842105 -0.234568 \n",
"77 0.785441 -0.061728 \n",
"82 0.717105 -0.382716 \n",
"87 0.850877 -0.037037 \n",
"92 0.770335 -0.024691 \n",
"97 0.684211 NaN \n",
"99 0.753849 -0.127572 \n",
"104 0.667215 -0.037037 \n",
"109 NaN NaN \n",
"110 0.894737 -0.407407 \n",
"112 0.890907 -0.078189 \n",
"117 NaN NaN \n",
"119 0.744919 -0.098765 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN \\\n",
"0 -0.230769 -0.283019 \n",
"4 -0.830769 -0.014151 \n",
"8 -0.482051 -0.459119 \n",
"13 -0.102564 0.160377 \n",
"18 -0.492308 -0.449686 \n",
"23 -0.220513 -0.270440 \n",
"28 -0.415385 -0.540881 \n",
"33 -0.630769 -0.320755 \n",
"38 -0.076923 -0.481132 \n",
"43 -0.292308 -0.754717 \n",
"48 -0.246154 -0.415094 \n",
"51 -0.820513 -0.264151 \n",
"56 -0.461538 -0.712264 \n",
"60 -0.553846 -0.641509 \n",
"62 -0.661538 -0.169811 \n",
"66 -0.384615 -0.358491 \n",
"71 NaN NaN \n",
"73 0.123077 -0.811321 \n",
"77 -0.600000 -0.374214 \n",
"82 -0.707692 -0.320755 \n",
"87 -0.141026 -0.301887 \n",
"92 -0.461538 -0.179245 \n",
"97 NaN -0.358491 \n",
"99 -0.364103 -0.440252 \n",
"104 -0.538462 -0.216981 \n",
"109 NaN NaN \n",
"110 -0.615385 -0.660377 \n",
"112 -0.487179 -0.556604 \n",
"117 NaN NaN \n",
"119 -0.461538 -0.132075 \n",
"\n",
" RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN OXYGEN_SATURATION_MEDIAN \\\n",
"0 -0.586207 -0.285714 0.736842 \n",
"4 -0.620690 0.357143 0.881579 \n",
"8 -0.494253 -0.142857 0.666667 \n",
"13 -0.540230 0.226190 0.833333 \n",
"18 -0.494253 -0.136905 0.833333 \n",
"23 -0.517241 0.238095 0.736842 \n",
"28 -0.471264 -0.154762 0.807018 \n",
"33 -0.586207 0.464286 0.771930 \n",
"38 -0.517241 -0.089286 0.763158 \n",
"43 -0.655172 0.107143 0.789474 \n",
"48 -0.379310 0.785714 0.684211 \n",
"51 -0.540230 -0.107143 0.877193 \n",
"56 -0.568966 -0.080357 0.684211 \n",
"60 -0.379310 -0.178571 0.789474 \n",
"62 -0.344828 0.017857 0.631579 \n",
"66 -0.563218 -0.011905 0.754386 \n",
"71 NaN NaN NaN \n",
"73 -0.172414 -0.107143 0.842105 \n",
"77 -0.551724 0.160714 0.789474 \n",
"82 -0.551724 0.178571 0.710526 \n",
"87 -0.540230 -0.047619 0.885965 \n",
"92 -0.540230 0.202381 0.771930 \n",
"97 NaN 0.214286 0.684211 \n",
"99 -0.655172 0.119048 0.754386 \n",
"104 -0.551724 0.226190 0.684211 \n",
"109 NaN NaN NaN \n",
"110 -0.517241 0.178571 0.894737 \n",
"112 -0.494253 -0.166667 0.947368 \n",
"117 NaN NaN NaN \n",
"119 -0.517241 0.071429 0.763158 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MIN BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN \\\n",
"0 0.237113 0.000000 -0.162393 \n",
"4 -0.515464 -0.587500 -0.145299 \n",
"8 -0.127148 -0.329167 -0.435897 \n",
"13 0.237113 0.000000 0.048433 \n",
"18 0.010309 -0.291667 -0.418803 \n",
"23 0.030928 -0.141667 -0.236467 \n",
"28 0.051546 -0.233333 -0.487179 \n",
"33 0.010309 -0.325000 -0.196581 \n",
"38 0.030928 0.000000 -0.376068 \n",
"43 0.381443 -0.050000 -0.589744 \n",
"48 0.195876 -0.012500 -0.282051 \n",
"51 -0.106529 -0.541667 -0.310541 \n",
"56 -0.164948 -0.206250 -0.555556 \n",
"60 -0.030928 -0.262500 -0.487179 \n",
"62 0.020619 -0.350000 -0.094017 \n",
"66 0.168385 -0.208333 -0.333333 \n",
"71 NaN NaN NaN \n",
"73 -0.030928 0.287500 -0.641026 \n",
"77 -0.024055 -0.425000 -0.396011 \n",
"82 -0.154639 -0.418750 -0.290598 \n",
"87 0.037801 -0.041667 -0.242165 \n",
"92 -0.037801 -0.291667 -0.156695 \n",
"97 NaN NaN -0.230769 \n",
"99 -0.010309 -0.191667 -0.367521 \n",
"104 0.030928 -0.312500 -0.239316 \n",
"109 NaN NaN NaN \n",
"110 -0.175258 -0.312500 -0.504274 \n",
"112 -0.085911 -0.291667 -0.512821 \n",
"117 NaN NaN NaN \n",
"119 -0.072165 -0.250000 -0.119658 \n",
"\n",
" RESPIRATORY_RATE_MIN TEMPERATURE_MIN OXYGEN_SATURATION_MIN \\\n",
"0 -0.500000 0.208791 0.898990 \n",
"4 -0.678571 0.604396 0.878788 \n",
"8 -0.547619 0.216117 0.629630 \n",
"13 -0.500000 0.362637 0.925926 \n",
"18 -0.404762 0.252747 0.925926 \n",
"23 -0.535714 0.516484 0.313131 \n",
"28 -0.428571 0.238095 0.912458 \n",
"33 -0.500000 0.670330 0.865320 \n",
"38 -0.571429 0.219780 0.909091 \n",
"43 -0.571429 0.450549 0.919192 \n",
"48 -0.285714 0.868132 0.878788 \n",
"51 -0.500000 0.164835 0.925926 \n",
"56 -0.500000 0.307692 0.868687 \n",
"60 -0.285714 0.274725 0.919192 \n",
"62 -0.285714 0.362637 0.828283 \n",
"66 -0.523810 0.318681 0.878788 \n",
"71 NaN NaN NaN \n",
"73 -0.071429 0.318681 0.939394 \n",
"77 -0.535714 0.390110 0.893939 \n",
"82 -0.500000 0.406593 0.868687 \n",
"87 -0.523810 0.260073 0.360269 \n",
"92 -0.500000 0.355311 0.885522 \n",
"97 NaN 0.516484 0.878788 \n",
"99 -0.619048 0.333333 0.878788 \n",
"104 -0.535714 0.472527 0.831650 \n",
"109 NaN NaN NaN \n",
"110 -0.428571 0.494505 0.959596 \n",
"112 -0.547619 0.164835 0.878788 \n",
"117 NaN NaN NaN \n",
"119 -0.464286 0.362637 0.838384 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MAX BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX \\\n",
"0 -0.247863 -0.459459 -0.432836 \n",
"4 -0.504274 -0.627027 0.000000 \n",
"8 -0.247863 -0.506306 -0.263682 \n",
"13 -0.076923 -0.293694 0.019900 \n",
"18 -0.282051 -0.553153 -0.482587 \n",
"23 -0.373219 -0.452252 -0.393035 \n",
"28 -0.270655 -0.513514 -0.358209 \n",
"33 -0.435897 -0.740541 -0.462687 \n",
"38 -0.247863 -0.297297 -0.522388 \n",
"43 -0.128205 -0.502703 -0.805970 \n",
"48 -0.282051 -0.470270 -0.537313 \n",
"51 -0.418803 -0.783784 -0.353234 \n",
"56 -0.529915 -0.605405 -0.768657 \n",
"60 -0.470085 -0.686486 -0.716418 \n",
"62 -0.384615 -0.708108 -0.343284 \n",
"66 -0.133903 -0.531532 -0.388060 \n",
"71 NaN NaN NaN \n",
"73 -0.470085 -0.210811 -0.850746 \n",
"77 -0.242165 -0.614414 -0.378109 \n",
"82 -0.401709 -0.708108 -0.417910 \n",
"87 -0.179487 -0.290090 -0.333333 \n",
"92 -0.259259 -0.563964 -0.233831 \n",
"97 NaN NaN -0.492537 \n",
"99 -0.339031 -0.445045 -0.467662 \n",
"104 -0.162393 -0.556757 -0.194030 \n",
"109 NaN NaN NaN \n",
"110 -0.589744 -0.729730 -0.731343 \n",
"112 -0.304843 -0.477477 -0.567164 \n",
"117 NaN NaN NaN \n",
"119 -0.239316 -0.486486 -0.119403 \n",
"\n",
" RESPIRATORY_RATE_MAX TEMPERATURE_MAX OXYGEN_SATURATION_MAX \\\n",
"0 -0.636364 -0.420290 0.736842 \n",
"4 -0.636364 0.101449 1.000000 \n",
"8 -0.454545 -0.024155 0.754386 \n",
"13 -0.535354 0.072464 0.912281 \n",
"18 -0.535354 -0.246377 0.894737 \n",
"23 -0.575758 0.120773 0.824561 \n",
"28 -0.515152 -0.246377 0.859649 \n",
"33 -0.636364 0.188406 0.789474 \n",
"38 -0.484848 -0.202899 0.868421 \n",
"43 -0.696970 -0.101449 0.789474 \n",
"48 -0.454545 0.449275 0.684211 \n",
"51 -0.515152 -0.173913 0.912281 \n",
"56 -0.606061 -0.217391 0.710526 \n",
"60 -0.454545 -0.333333 0.789474 \n",
"62 -0.424242 -0.173913 0.631579 \n",
"66 -0.575758 -0.033816 0.807018 \n",
"71 NaN NaN NaN \n",
"73 -0.272727 -0.275362 0.842105 \n",
"77 -0.242424 0.050725 0.868421 \n",
"82 -0.363636 0.202899 0.789474 \n",
"87 -0.535354 -0.072464 0.929825 \n",
"92 -0.434343 0.082126 0.842105 \n",
"97 NaN -0.014493 0.684211 \n",
"99 -0.616162 0.101449 0.807018 \n",
"104 -0.424242 0.159420 0.719298 \n",
"109 NaN NaN NaN \n",
"110 -0.575758 -0.043478 0.894737 \n",
"112 -0.494949 -0.256039 1.000000 \n",
"117 NaN NaN NaN \n",
"119 -0.484848 0.260870 0.842105 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF BLOODPRESSURE_SISTOLIC_DIFF \\\n",
"0 -1.000000 -1.000000 \n",
"4 -0.626087 -0.613497 \n",
"8 -0.692754 -0.730061 \n",
"13 -0.826087 -0.811861 \n",
"18 -0.843478 -0.820041 \n",
"23 -0.953623 -0.852761 \n",
"28 -0.866667 -0.832311 \n",
"33 -1.000000 -1.000000 \n",
"38 -0.826087 -0.815951 \n",
"43 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 \n",
"51 -0.884058 -0.836401 \n",
"56 -0.947826 -0.963190 \n",
"60 -1.000000 -1.000000 \n",
"62 -0.956522 -0.938650 \n",
"66 -0.826087 -0.877301 \n",
"71 NaN NaN \n",
"73 -1.000000 -1.000000 \n",
"77 -0.773913 -0.758691 \n",
"82 -0.826087 -0.871166 \n",
"87 -0.762319 -0.766871 \n",
"92 -0.779710 -0.832311 \n",
"97 NaN NaN \n",
"99 -0.884058 -0.795501 \n",
"104 -0.739130 -0.803681 \n",
"109 NaN NaN \n",
"110 -1.000000 -1.000000 \n",
"112 -0.785507 -0.734151 \n",
"117 NaN NaN \n",
"119 -0.730435 -0.785276 \n",
"\n",
" HEART_RATE_DIFF RESPIRATORY_RATE_DIFF TEMPERATURE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"4 -0.572519 -0.852941 -1.000000 \n",
"8 -0.582697 -0.784314 -0.682540 \n",
"13 -0.725191 -0.901961 -0.761905 \n",
"18 -0.821883 -0.980392 -0.904762 \n",
"23 -0.893130 -0.911765 -0.888889 \n",
"28 -0.633588 -0.941176 -0.888889 \n",
"33 -1.000000 -1.000000 -1.000000 \n",
"38 -0.900763 -0.794118 -0.833333 \n",
"43 -1.000000 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 -1.000000 \n",
"51 -0.786260 -0.882353 -0.750000 \n",
"56 -0.992366 -0.970588 -0.940476 \n",
"60 -1.000000 -1.000000 -1.000000 \n",
"62 -0.969466 -0.970588 -0.964286 \n",
"66 -0.801527 -0.921569 -0.801587 \n",
"71 NaN NaN NaN \n",
"73 -1.000000 -1.000000 -1.000000 \n",
"77 -0.735369 -0.588235 -0.809524 \n",
"82 -0.870229 -0.735294 -0.702381 \n",
"87 -0.826972 -0.882353 -0.769841 \n",
"92 -0.801527 -0.803922 -0.746032 \n",
"97 -1.000000 NaN -1.000000 \n",
"99 -0.852417 -0.882353 -0.706349 \n",
"104 -0.687023 -0.764706 -0.809524 \n",
"109 NaN NaN NaN \n",
"110 -1.000000 -1.000000 -1.000000 \n",
"112 -0.824427 -0.823529 -0.817460 \n",
"117 NaN NaN NaN \n",
"119 -0.717557 -0.882353 -0.607143 \n",
"\n",
" OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"4 -0.878788 -0.587195 \n",
"8 -0.723906 -0.769565 \n",
"13 -0.959596 -0.884058 \n",
"18 -0.966330 -0.873174 \n",
"23 -0.380471 -0.961353 \n",
"28 -0.966330 -0.891551 \n",
"33 -0.946128 -1.000000 \n",
"38 -0.959596 -0.869565 \n",
"43 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 \n",
"51 -0.959596 -0.899181 \n",
"56 -0.979798 -0.951087 \n",
"60 -1.000000 -1.000000 \n",
"62 -0.969697 -0.965217 \n",
"66 -0.952862 -0.850932 \n",
"71 NaN NaN \n",
"73 -1.000000 -1.000000 \n",
"77 -0.944444 -0.830435 \n",
"82 -0.949495 -0.826087 \n",
"87 -0.387205 -0.796273 \n",
"92 -0.946128 -0.832197 \n",
"97 -1.000000 NaN \n",
"99 -0.952862 -0.900621 \n",
"104 -0.939394 -0.776398 \n",
"109 NaN NaN \n",
"110 -1.000000 -1.000000 \n",
"112 -0.878788 -0.816149 \n",
"117 NaN NaN \n",
"119 -0.898990 -0.784348 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"4 -0.457543 -0.734417 \n",
"8 -0.685906 -0.689698 \n",
"13 -0.826611 -0.839287 \n",
"18 -0.799242 -0.856110 \n",
"23 -0.879195 -0.927077 \n",
"28 -0.817431 -0.686446 \n",
"33 -1.000000 -1.000000 \n",
"38 -0.831735 -0.904071 \n",
"43 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 \n",
"51 -0.779583 -0.850639 \n",
"56 -0.960738 -0.991567 \n",
"60 -1.000000 -1.000000 \n",
"62 -0.921477 -0.978136 \n",
"66 -0.869128 -0.830714 \n",
"71 NaN NaN \n",
"73 -1.000000 -1.000000 \n",
"77 -0.731427 -0.816184 \n",
"82 -0.842953 -0.902250 \n",
"87 -0.765969 -0.867952 \n",
"92 -0.817330 -0.851997 \n",
"97 NaN -1.000000 \n",
"99 -0.781879 -0.882743 \n",
"104 -0.771568 -0.751333 \n",
"109 NaN NaN \n",
"110 -1.000000 -1.000000 \n",
"112 -0.692251 -0.845347 \n",
"117 NaN NaN \n",
"119 -0.750153 -0.789978 \n",
"\n",
" RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"4 -0.841577 -1.000000 \n",
"8 -0.776583 -0.682540 \n",
"13 -0.896057 -0.766042 \n",
"18 -0.979689 -0.904177 \n",
"23 -0.913413 -0.890700 \n",
"28 -0.939068 -0.888823 \n",
"33 -1.000000 -1.000000 \n",
"38 -0.808065 -0.835604 \n",
"43 -1.000000 -1.000000 \n",
"48 -1.000000 -1.000000 \n",
"51 -0.870968 -0.749307 \n",
"56 -0.970357 -0.939045 \n",
"60 -1.000000 -1.000000 \n",
"62 -0.971138 -0.963786 \n",
"66 -0.918757 -0.802677 \n",
"71 NaN NaN \n",
"73 -1.000000 -1.000000 \n",
"77 -0.573477 -0.812629 \n",
"82 -0.709677 -0.703201 \n",
"87 -0.878136 -0.770475 \n",
"92 -0.797519 -0.750556 \n",
"97 NaN -1.000000 \n",
"99 -0.878136 -0.711137 \n",
"104 -0.756272 -0.810979 \n",
"109 NaN NaN \n",
"110 -1.000000 -1.000000 \n",
"112 -0.819342 -0.816042 \n",
"117 NaN NaN \n",
"119 -0.878136 -0.609948 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"0 -1.000000 0.0 0.0 \n",
"4 -0.881277 1.0 0.0 \n",
"8 -0.724145 0.0 0.0 \n",
"13 -0.960291 1.0 0.0 \n",
"18 -0.967019 1.0 0.0 \n",
"23 -0.360825 0.0 0.0 \n",
"28 -0.966710 0.0 1.0 \n",
"33 -0.946233 0.0 0.0 \n",
"38 -0.959202 0.0 0.0 \n",
"43 -1.000000 0.0 0.0 \n",
"48 -1.000000 0.0 0.0 \n",
"51 -0.960463 0.0 1.0 \n",
"56 -0.979816 0.0 0.0 \n",
"60 -1.000000 0.0 0.0 \n",
"62 -0.969072 0.0 0.0 \n",
"66 -0.952903 0.0 0.0 \n",
"71 NaN 0.0 1.0 \n",
"73 -1.000000 0.0 0.0 \n",
"77 -0.943902 0.0 0.0 \n",
"82 -0.949539 0.0 0.0 \n",
"87 -0.397254 0.0 0.0 \n",
"92 -0.946315 0.0 0.0 \n",
"97 -1.000000 0.0 0.0 \n",
"99 -0.952402 0.0 0.0 \n",
"104 -0.938802 0.0 1.0 \n",
"109 NaN 0.0 0.0 \n",
"110 -1.000000 0.0 0.0 \n",
"112 -0.883579 0.0 0.0 \n",
"117 NaN 0.0 0.0 \n",
"119 -0.899078 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"8 0.0 1.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"23 0.0 0.0 0.0 \n",
"28 0.0 0.0 0.0 \n",
"33 0.0 0.0 1.0 \n",
"38 0.0 0.0 0.0 \n",
"43 0.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 \n",
"51 0.0 0.0 0.0 \n",
"56 0.0 0.0 1.0 \n",
"60 0.0 0.0 0.0 \n",
"62 0.0 0.0 1.0 \n",
"66 1.0 0.0 0.0 \n",
"71 0.0 0.0 0.0 \n",
"73 0.0 0.0 0.0 \n",
"77 0.0 1.0 0.0 \n",
"82 0.0 0.0 0.0 \n",
"87 0.0 0.0 0.0 \n",
"92 0.0 1.0 0.0 \n",
"97 0.0 0.0 0.0 \n",
"99 0.0 0.0 0.0 \n",
"104 0.0 0.0 0.0 \n",
"109 0.0 1.0 0.0 \n",
"110 0.0 0.0 0.0 \n",
"112 0.0 0.0 0.0 \n",
"117 0.0 0.0 0.0 \n",
"119 1.0 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"0 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"23 0.0 1.0 0.0 \n",
"28 0.0 0.0 0.0 \n",
"33 0.0 0.0 0.0 \n",
"38 0.0 0.0 1.0 \n",
"43 0.0 0.0 1.0 \n",
"48 0.0 0.0 1.0 \n",
"51 0.0 0.0 0.0 \n",
"56 0.0 0.0 0.0 \n",
"60 1.0 0.0 0.0 \n",
"62 0.0 0.0 0.0 \n",
"66 0.0 0.0 0.0 \n",
"71 0.0 0.0 0.0 \n",
"73 0.0 1.0 0.0 \n",
"77 0.0 0.0 0.0 \n",
"82 0.0 1.0 0.0 \n",
"87 1.0 0.0 0.0 \n",
"92 0.0 0.0 0.0 \n",
"97 0.0 0.0 1.0 \n",
"99 0.0 0.0 0.0 \n",
"104 0.0 0.0 0.0 \n",
"109 0.0 0.0 0.0 \n",
"110 0.0 1.0 0.0 \n",
"112 0.0 0.0 1.0 \n",
"117 0.0 0.0 0.0 \n",
"119 0.0 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th WINDOW_0-2 WINDOW_2-4 \\\n",
"0 0.0 0.0 1.0 0.0 \n",
"4 0.0 0.0 1.0 0.0 \n",
"8 0.0 0.0 1.0 0.0 \n",
"13 0.0 0.0 1.0 0.0 \n",
"18 0.0 0.0 1.0 0.0 \n",
"23 0.0 0.0 1.0 0.0 \n",
"28 0.0 0.0 1.0 0.0 \n",
"33 0.0 0.0 1.0 0.0 \n",
"38 0.0 0.0 1.0 0.0 \n",
"43 0.0 0.0 1.0 0.0 \n",
"48 0.0 0.0 1.0 0.0 \n",
"51 0.0 0.0 1.0 0.0 \n",
"56 0.0 0.0 1.0 0.0 \n",
"60 0.0 0.0 1.0 0.0 \n",
"62 0.0 0.0 1.0 0.0 \n",
"66 0.0 0.0 1.0 0.0 \n",
"71 0.0 0.0 1.0 0.0 \n",
"73 0.0 0.0 1.0 0.0 \n",
"77 0.0 0.0 1.0 0.0 \n",
"82 0.0 0.0 1.0 0.0 \n",
"87 0.0 0.0 1.0 0.0 \n",
"92 0.0 0.0 1.0 0.0 \n",
"97 0.0 0.0 1.0 0.0 \n",
"99 1.0 0.0 1.0 0.0 \n",
"104 0.0 0.0 1.0 0.0 \n",
"109 0.0 0.0 1.0 0.0 \n",
"110 0.0 0.0 1.0 0.0 \n",
"112 0.0 0.0 1.0 0.0 \n",
"117 1.0 0.0 1.0 0.0 \n",
"119 0.0 0.0 1.0 0.0 \n",
"\n",
" WINDOW_4-6 WINDOW_6-12 WINDOW_ABOVE_12 ICU \n",
"0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 \n",
"23 0.0 0.0 0.0 0.0 \n",
"28 0.0 0.0 0.0 0.0 \n",
"33 0.0 0.0 0.0 0.0 \n",
"38 0.0 0.0 0.0 0.0 \n",
"43 0.0 0.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 1.0 \n",
"51 0.0 0.0 0.0 0.0 \n",
"56 0.0 0.0 0.0 1.0 \n",
"60 0.0 0.0 0.0 1.0 \n",
"62 0.0 0.0 0.0 1.0 \n",
"66 0.0 0.0 0.0 0.0 \n",
"71 0.0 0.0 0.0 1.0 \n",
"73 0.0 0.0 0.0 1.0 \n",
"77 0.0 0.0 0.0 0.0 \n",
"82 0.0 0.0 0.0 0.0 \n",
"87 0.0 0.0 0.0 0.0 \n",
"92 0.0 0.0 0.0 0.0 \n",
"97 0.0 0.0 0.0 1.0 \n",
"99 0.0 0.0 0.0 0.0 \n",
"104 0.0 0.0 0.0 0.0 \n",
"109 0.0 0.0 0.0 1.0 \n",
"110 0.0 0.0 0.0 1.0 \n",
"112 0.0 0.0 0.0 0.0 \n",
"117 0.0 0.0 0.0 1.0 \n",
"119 0.0 0.0 0.0 0.0 "
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 249
},
"id": "KhMGh228J4Yg",
"outputId": "ff583d95-a9cc-4b1d-a8b4-a615de05377e"
},
"source": [
"final_data = final_data.drop(['GENDER','PATIENT_VISIT_IDENTIFIER','WINDOW_0-2',\t'WINDOW_2-4',\t'WINDOW_4-6',\t'WINDOW_6-12',\t'WINDOW_ABOVE_12'],axis = 1)\n",
"final_data.head()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>0.183673</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.868365</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-0.945093</td>\n",
" <td>-1.0</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>0.090147</td>\n",
" <td>-1.0</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>0.109756</td>\n",
" <td>-1.0</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-0.932246</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.835844</td>\n",
" <td>-1.0</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.914938</td>\n",
" <td>-1.0</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.868747</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.875236</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-0.540721</td>\n",
" <td>-1.0</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-0.518519</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.028571</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-0.997201</td>\n",
" <td>-1.0</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-0.990854</td>\n",
" <td>-1.0</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-0.825613</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-0.994912</td>\n",
" <td>-1.0</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.593220</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.283019</td>\n",
" <td>-0.586207</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.237113</td>\n",
" <td>0.000000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>0.898990</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>-0.636364</td>\n",
" <td>-0.420290</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-0.938950</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>0.357143</td>\n",
" <td>-1.0</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.912243</td>\n",
" <td>-1.0</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-0.742004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-0.958528</td>\n",
" <td>-1.0</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.780261</td>\n",
" <td>-1.0</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>0.144654</td>\n",
" <td>-1.0</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>0.158537</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.382773</td>\n",
" <td>-1.0</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.908714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.412965</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.939887</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-0.399199</td>\n",
" <td>-1.0</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-0.703704</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-0.995428</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.836145</td>\n",
" <td>-1.0</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-0.978029</td>\n",
" <td>-1.0</td>\n",
" <td>-0.551183</td>\n",
" <td>-0.757158</td>\n",
" <td>-0.005241</td>\n",
" <td>-0.683145</td>\n",
" <td>0.357143</td>\n",
" <td>0.867507</td>\n",
" <td>-0.555556</td>\n",
" <td>-0.830769</td>\n",
" <td>-0.014151</td>\n",
" <td>-0.620690</td>\n",
" <td>0.357143</td>\n",
" <td>0.881579</td>\n",
" <td>-0.515464</td>\n",
" <td>-0.587500</td>\n",
" <td>-0.145299</td>\n",
" <td>-0.678571</td>\n",
" <td>0.604396</td>\n",
" <td>0.878788</td>\n",
" <td>-0.504274</td>\n",
" <td>-0.627027</td>\n",
" <td>0.000000</td>\n",
" <td>-0.636364</td>\n",
" <td>0.101449</td>\n",
" <td>1.000000</td>\n",
" <td>-0.626087</td>\n",
" <td>-0.613497</td>\n",
" <td>-0.572519</td>\n",
" <td>-0.852941</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.878788</td>\n",
" <td>-0.587195</td>\n",
" <td>-0.457543</td>\n",
" <td>-0.734417</td>\n",
" <td>-0.841577</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.881277</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>-0.317073</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>-1.0</td>\n",
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" <td>-1.0</td>\n",
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" <td>0.189142</td>\n",
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" <td>0.251029</td>\n",
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" <td>0.160377</td>\n",
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" <td>0.833333</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
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" <td>-0.891993</td>\n",
" <td>-0.891993</td>\n",
" <td>-1.0</td>\n",
" <td>0.291405</td>\n",
" <td>0.291405</td>\n",
" <td>0.291405</td>\n",
" <td>0.291405</td>\n",
" <td>-1.0</td>\n",
" <td>0.243902</td>\n",
" <td>0.243902</td>\n",
" <td>0.243902</td>\n",
" <td>0.243902</td>\n",
" <td>-1.0</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.959849</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.706450</td>\n",
" <td>-0.706450</td>\n",
" <td>-0.706450</td>\n",
" <td>-0.706450</td>\n",
" <td>-1.0</td>\n",
" <td>-0.340249</td>\n",
" <td>-0.340249</td>\n",
" <td>-0.340249</td>\n",
" <td>-0.340249</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846339</td>\n",
" <td>-0.846339</td>\n",
" <td>-0.846339</td>\n",
" <td>-0.846339</td>\n",
" <td>-1.0</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-0.170732</td>\n",
" <td>-1.0</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-0.704142</td>\n",
" <td>-1.0</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-0.77931</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997732</td>\n",
" <td>-0.997732</td>\n",
" <td>-0.997732</td>\n",
" <td>-0.997732</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.292390</td>\n",
" <td>-0.292390</td>\n",
" <td>-0.292390</td>\n",
" <td>-0.292390</td>\n",
" <td>-1.0</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-0.666667</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>0.085714</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997947</td>\n",
" <td>-0.997947</td>\n",
" <td>-0.997947</td>\n",
" <td>-0.997947</td>\n",
" <td>-1.0</td>\n",
" <td>-0.988948</td>\n",
" <td>-0.988948</td>\n",
" <td>-0.988948</td>\n",
" <td>-0.988948</td>\n",
" <td>-1.0</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-0.846633</td>\n",
" <td>-1.0</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.884337</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.078189</td>\n",
" <td>-0.494587</td>\n",
" <td>-0.454228</td>\n",
" <td>-0.495292</td>\n",
" <td>-0.137566</td>\n",
" <td>0.835283</td>\n",
" <td>-0.090535</td>\n",
" <td>-0.492308</td>\n",
" <td>-0.449686</td>\n",
" <td>-0.494253</td>\n",
" <td>-0.136905</td>\n",
" <td>0.833333</td>\n",
" <td>0.010309</td>\n",
" <td>-0.291667</td>\n",
" <td>-0.418803</td>\n",
" <td>-0.404762</td>\n",
" <td>0.252747</td>\n",
" <td>0.925926</td>\n",
" <td>-0.282051</td>\n",
" <td>-0.553153</td>\n",
" <td>-0.482587</td>\n",
" <td>-0.535354</td>\n",
" <td>-0.246377</td>\n",
" <td>0.894737</td>\n",
" <td>-0.843478</td>\n",
" <td>-0.820041</td>\n",
" <td>-0.821883</td>\n",
" <td>-0.980392</td>\n",
" <td>-0.904762</td>\n",
" <td>-0.966330</td>\n",
" <td>-0.873174</td>\n",
" <td>-0.799242</td>\n",
" <td>-0.856110</td>\n",
" <td>-0.979689</td>\n",
" <td>-0.904177</td>\n",
" <td>-0.967019</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n",
"0 1.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 \n",
"\n",
" DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 HTN \\\n",
"0 0.0 1.0 1.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 \n",
"\n",
" IMMUNOCOMPROMISED OTHER ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN \\\n",
"0 0.0 1.0 0.605263 0.605263 0.605263 \n",
"4 0.0 1.0 0.605263 0.605263 0.605263 \n",
"8 1.0 1.0 -0.263158 -0.263158 -0.263158 \n",
"13 0.0 1.0 0.605263 0.605263 0.605263 \n",
"18 0.0 1.0 0.605263 0.605263 0.605263 \n",
"\n",
" ALBUMIN_MAX ALBUMIN_DIFF BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN \\\n",
"0 0.605263 -1.0 -1.0 -1.0 \n",
"4 0.605263 -1.0 -1.0 -1.0 \n",
"8 -0.263158 -1.0 -1.0 -1.0 \n",
"13 0.605263 -1.0 -1.0 -1.0 \n",
"18 0.605263 -1.0 -1.0 -1.0 \n",
"\n",
" BE_ARTERIAL_MIN BE_ARTERIAL_MAX BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN \\\n",
"0 -1.0 -1.0 -1.0 -1.0 \n",
"4 -1.0 -1.0 -1.0 -1.0 \n",
"8 -1.0 -1.0 -1.0 -1.0 \n",
"13 -1.0 -1.0 -1.0 -1.0 \n",
"18 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" BE_VENOUS_MEAN BE_VENOUS_MIN BE_VENOUS_MAX BE_VENOUS_DIFF \\\n",
"0 -1.0 -1.0 -1.0 -1.0 \n",
"4 -1.0 -1.0 -1.0 -1.0 \n",
"8 -1.0 -1.0 -1.0 -1.0 \n",
"13 -1.0 -1.0 -1.0 -1.0 \n",
"18 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN BIC_ARTERIAL_MIN \\\n",
"0 -0.317073 -0.317073 -0.317073 \n",
"4 -0.317073 -0.317073 -0.317073 \n",
"8 -0.317073 -0.317073 -0.317073 \n",
"13 -0.317073 -0.317073 -0.317073 \n",
"18 -0.317073 -0.317073 -0.317073 \n",
"\n",
" BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF BIC_VENOUS_MEDIAN BIC_VENOUS_MEAN \\\n",
"0 -0.317073 -1.0 -0.317073 -0.317073 \n",
"4 -0.317073 -1.0 -0.317073 -0.317073 \n",
"8 -0.317073 -1.0 -0.317073 -0.317073 \n",
"13 -0.317073 -1.0 -0.317073 -0.317073 \n",
"18 -0.317073 -1.0 -0.317073 -0.317073 \n",
"\n",
" BIC_VENOUS_MIN BIC_VENOUS_MAX BIC_VENOUS_DIFF BILLIRUBIN_MEDIAN \\\n",
"0 -0.317073 -0.317073 -1.0 -0.938950 \n",
"4 -0.317073 -0.317073 -1.0 -0.938950 \n",
"8 -0.317073 -0.317073 -1.0 -0.972789 \n",
"13 -0.317073 -0.317073 -1.0 -0.935113 \n",
"18 -0.317073 -0.317073 -1.0 -0.938950 \n",
"\n",
" BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX BILLIRUBIN_DIFF \\\n",
"0 -0.938950 -0.938950 -0.938950 -1.0 \n",
"4 -0.938950 -0.938950 -0.938950 -1.0 \n",
"8 -0.972789 -0.972789 -0.972789 -1.0 \n",
"13 -0.935113 -0.935113 -0.935113 -1.0 \n",
"18 -0.938950 -0.938950 -0.938950 -1.0 \n",
"\n",
" BLAST_MEDIAN BLAST_MEAN BLAST_MIN BLAST_MAX BLAST_DIFF \\\n",
"0 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"4 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"8 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"13 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"18 -1.0 -1.0 -1.0 -1.0 -1.0 \n",
"\n",
" CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX CALCIUM_DIFF \\\n",
"0 0.183673 0.183673 0.183673 0.183673 -1.0 \n",
"4 0.357143 0.357143 0.357143 0.357143 -1.0 \n",
"8 0.326531 0.326531 0.326531 0.326531 -1.0 \n",
"13 0.357143 0.357143 0.357143 0.357143 -1.0 \n",
"18 0.357143 0.357143 0.357143 0.357143 -1.0 \n",
"\n",
" CREATININ_MEDIAN CREATININ_MEAN CREATININ_MIN CREATININ_MAX \\\n",
"0 -0.868365 -0.868365 -0.868365 -0.868365 \n",
"4 -0.912243 -0.912243 -0.912243 -0.912243 \n",
"8 -0.968861 -0.968861 -0.968861 -0.968861 \n",
"13 -0.913659 -0.913659 -0.913659 -0.913659 \n",
"18 -0.891012 -0.891012 -0.891012 -0.891012 \n",
"\n",
" CREATININ_DIFF FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX FFA_DIFF \\\n",
"0 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 -1.0 \n",
"4 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 -1.0 \n",
"8 -1.0 -0.194030 -0.194030 -0.194030 -0.194030 -1.0 \n",
"13 -1.0 -0.829424 -0.829424 -0.829424 -0.829424 -1.0 \n",
"18 -1.0 -0.742004 -0.742004 -0.742004 -0.742004 -1.0 \n",
"\n",
" GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF GLUCOSE_MEDIAN \\\n",
"0 -0.945093 -0.945093 -0.945093 -0.945093 -1.0 -0.891993 \n",
"4 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 -0.780261 \n",
"8 -0.316589 -0.316589 -0.316589 -0.316589 -1.0 -0.891993 \n",
"13 -0.938084 -0.938084 -0.938084 -0.938084 -1.0 -0.851024 \n",
"18 -0.958528 -0.958528 -0.958528 -0.958528 -1.0 -0.891993 \n",
"\n",
" GLUCOSE_MEAN GLUCOSE_MIN GLUCOSE_MAX GLUCOSE_DIFF HEMATOCRITE_MEDIAN \\\n",
"0 -0.891993 -0.891993 -0.891993 -1.0 0.090147 \n",
"4 -0.780261 -0.780261 -0.780261 -1.0 0.144654 \n",
"8 -0.891993 -0.891993 -0.891993 -1.0 -0.203354 \n",
"13 -0.851024 -0.851024 -0.851024 -1.0 0.358491 \n",
"18 -0.891993 -0.891993 -0.891993 -1.0 0.291405 \n",
"\n",
" HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX HEMATOCRITE_DIFF \\\n",
"0 0.090147 0.090147 0.090147 -1.0 \n",
"4 0.144654 0.144654 0.144654 -1.0 \n",
"8 -0.203354 -0.203354 -0.203354 -1.0 \n",
"13 0.358491 0.358491 0.358491 -1.0 \n",
"18 0.291405 0.291405 0.291405 -1.0 \n",
"\n",
" HEMOGLOBIN_MEDIAN HEMOGLOBIN_MEAN HEMOGLOBIN_MIN HEMOGLOBIN_MAX \\\n",
"0 0.109756 0.109756 0.109756 0.109756 \n",
"4 0.158537 0.158537 0.158537 0.158537 \n",
"8 -0.219512 -0.219512 -0.219512 -0.219512 \n",
"13 0.304878 0.304878 0.304878 0.304878 \n",
"18 0.243902 0.243902 0.243902 0.243902 \n",
"\n",
" HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN INR_MAX INR_DIFF \\\n",
"0 -1.0 -0.932246 -0.932246 -0.932246 -0.932246 -1.0 \n",
"4 -1.0 -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"8 -1.0 -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"13 -1.0 -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"18 -1.0 -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"\n",
" LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX LACTATE_DIFF \\\n",
"0 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"4 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"8 -0.828421 -0.828421 -0.828421 -0.828421 -1.0 \n",
"13 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"18 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN LEUKOCYTES_MAX \\\n",
"0 -0.835844 -0.835844 -0.835844 -0.835844 \n",
"4 -0.382773 -0.382773 -0.382773 -0.382773 \n",
"8 -0.729239 -0.729239 -0.729239 -0.729239 \n",
"13 -0.702202 -0.702202 -0.702202 -0.702202 \n",
"18 -0.706450 -0.706450 -0.706450 -0.706450 \n",
"\n",
" LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN LINFOCITOS_MIN \\\n",
"0 -1.0 -0.914938 -0.914938 -0.914938 \n",
"4 -1.0 -0.908714 -0.908714 -0.908714 \n",
"8 -1.0 -0.836100 -0.836100 -0.836100 \n",
"13 -1.0 -0.641079 -0.641079 -0.641079 \n",
"18 -1.0 -0.340249 -0.340249 -0.340249 \n",
"\n",
" LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN NEUTROPHILES_MEAN \\\n",
"0 -0.914938 -1.0 -0.868747 -0.868747 \n",
"4 -0.908714 -1.0 -0.412965 -0.412965 \n",
"8 -0.836100 -1.0 -0.784714 -0.784714 \n",
"13 -0.641079 -1.0 -0.812725 -0.812725 \n",
"18 -0.340249 -1.0 -0.846339 -0.846339 \n",
"\n",
" NEUTROPHILES_MIN NEUTROPHILES_MAX NEUTROPHILES_DIFF \\\n",
"0 -0.868747 -0.868747 -1.0 \n",
"4 -0.412965 -0.412965 -1.0 \n",
"8 -0.784714 -0.784714 -1.0 \n",
"13 -0.812725 -0.812725 -1.0 \n",
"18 -0.846339 -0.846339 -1.0 \n",
"\n",
" P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN P02_ARTERIAL_MIN \\\n",
"0 -0.170732 -0.170732 -0.170732 \n",
"4 -0.170732 -0.170732 -0.170732 \n",
"8 -0.170732 -0.170732 -0.170732 \n",
"13 -0.170732 -0.170732 -0.170732 \n",
"18 -0.170732 -0.170732 -0.170732 \n",
"\n",
" P02_ARTERIAL_MAX P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN P02_VENOUS_MEAN \\\n",
"0 -0.170732 -1.0 -0.704142 -0.704142 \n",
"4 -0.170732 -1.0 -0.704142 -0.704142 \n",
"8 -0.170732 -1.0 -0.633136 -0.633136 \n",
"13 -0.170732 -1.0 -0.704142 -0.704142 \n",
"18 -0.170732 -1.0 -0.704142 -0.704142 \n",
"\n",
" P02_VENOUS_MIN P02_VENOUS_MAX P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN \\\n",
"0 -0.704142 -0.704142 -1.0 -0.77931 \n",
"4 -0.704142 -0.704142 -1.0 -0.77931 \n",
"8 -0.633136 -0.633136 -1.0 -0.77931 \n",
"13 -0.704142 -0.704142 -1.0 -0.77931 \n",
"18 -0.704142 -0.704142 -1.0 -0.77931 \n",
"\n",
" PC02_ARTERIAL_MEAN PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX \\\n",
"0 -0.77931 -0.77931 -0.77931 \n",
"4 -0.77931 -0.77931 -0.77931 \n",
"8 -0.77931 -0.77931 -0.77931 \n",
"13 -0.77931 -0.77931 -0.77931 \n",
"18 -0.77931 -0.77931 -0.77931 \n",
"\n",
" PC02_ARTERIAL_DIFF PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN \\\n",
"0 -1.0 -0.754601 -0.754601 -0.754601 \n",
"4 -1.0 -0.754601 -0.754601 -0.754601 \n",
"8 -1.0 -0.779141 -0.779141 -0.779141 \n",
"13 -1.0 -0.754601 -0.754601 -0.754601 \n",
"18 -1.0 -0.754601 -0.754601 -0.754601 \n",
"\n",
" PC02_VENOUS_MAX PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN \\\n",
"0 -0.754601 -1.0 -0.875236 -0.875236 -0.875236 \n",
"4 -0.754601 -1.0 -0.939887 -0.939887 -0.939887 \n",
"8 -0.779141 -1.0 -0.503592 -0.503592 -0.503592 \n",
"13 -0.754601 -1.0 -0.990926 -0.990926 -0.990926 \n",
"18 -0.754601 -1.0 -0.997732 -0.997732 -0.997732 \n",
"\n",
" PCR_MAX PCR_DIFF PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN \\\n",
"0 -0.875236 -1.0 0.234043 0.234043 0.234043 \n",
"4 -0.939887 -1.0 0.234043 0.234043 0.234043 \n",
"8 -0.503592 -1.0 0.234043 0.234043 0.234043 \n",
"13 -0.990926 -1.0 0.234043 0.234043 0.234043 \n",
"18 -0.997732 -1.0 0.234043 0.234043 0.234043 \n",
"\n",
" PH_ARTERIAL_MAX PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN \\\n",
"0 0.234043 -1.0 0.363636 0.363636 \n",
"4 0.234043 -1.0 0.363636 0.363636 \n",
"8 0.234043 -1.0 0.363636 0.363636 \n",
"13 0.234043 -1.0 0.363636 0.363636 \n",
"18 0.234043 -1.0 0.363636 0.363636 \n",
"\n",
" PH_VENOUS_MIN PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN \\\n",
"0 0.363636 0.363636 -1.0 -0.540721 \n",
"4 0.363636 0.363636 -1.0 -0.399199 \n",
"8 0.363636 0.363636 -1.0 -0.564753 \n",
"13 0.363636 0.363636 -1.0 -0.457944 \n",
"18 0.363636 0.363636 -1.0 -0.292390 \n",
"\n",
" PLATELETS_MEAN PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF \\\n",
"0 -0.540721 -0.540721 -0.540721 -1.0 \n",
"4 -0.399199 -0.399199 -0.399199 -1.0 \n",
"8 -0.564753 -0.564753 -0.564753 -1.0 \n",
"13 -0.457944 -0.457944 -0.457944 -1.0 \n",
"18 -0.292390 -0.292390 -0.292390 -1.0 \n",
"\n",
" POTASSIUM_MEDIAN POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX \\\n",
"0 -0.518519 -0.518519 -0.518519 -0.518519 \n",
"4 -0.703704 -0.703704 -0.703704 -0.703704 \n",
"8 -0.777778 -0.777778 -0.777778 -0.777778 \n",
"13 -0.592593 -0.592593 -0.592593 -0.592593 \n",
"18 -0.666667 -0.666667 -0.666667 -0.666667 \n",
"\n",
" POTASSIUM_DIFF SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN \\\n",
"0 -1.0 0.939394 0.939394 \n",
"4 -1.0 0.939394 0.939394 \n",
"8 -1.0 0.939394 0.939394 \n",
"13 -1.0 0.939394 0.939394 \n",
"18 -1.0 0.939394 0.939394 \n",
"\n",
" SAT02_ARTERIAL_MIN SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF \\\n",
"0 0.939394 0.939394 -1.0 \n",
"4 0.939394 0.939394 -1.0 \n",
"8 0.939394 0.939394 -1.0 \n",
"13 0.939394 0.939394 -1.0 \n",
"18 0.939394 0.939394 -1.0 \n",
"\n",
" SAT02_VENOUS_MEDIAN SAT02_VENOUS_MEAN SAT02_VENOUS_MIN \\\n",
"0 0.345679 0.345679 0.345679 \n",
"4 0.345679 0.345679 0.345679 \n",
"8 0.580247 0.580247 0.580247 \n",
"13 0.345679 0.345679 0.345679 \n",
"18 0.345679 0.345679 0.345679 \n",
"\n",
" SAT02_VENOUS_MAX SAT02_VENOUS_DIFF SODIUM_MEDIAN SODIUM_MEAN \\\n",
"0 0.345679 -1.0 -0.028571 -0.028571 \n",
"4 0.345679 -1.0 0.085714 0.085714 \n",
"8 0.580247 -1.0 0.200000 0.200000 \n",
"13 0.345679 -1.0 0.142857 0.142857 \n",
"18 0.345679 -1.0 0.085714 0.085714 \n",
"\n",
" SODIUM_MIN SODIUM_MAX SODIUM_DIFF TGO_MEDIAN TGO_MEAN TGO_MIN \\\n",
"0 -0.028571 -0.028571 -1.0 -0.997201 -0.997201 -0.997201 \n",
"4 0.085714 0.085714 -1.0 -0.995428 -0.995428 -0.995428 \n",
"8 0.200000 0.200000 -1.0 -0.989549 -0.989549 -0.989549 \n",
"13 0.142857 0.142857 -1.0 -0.998507 -0.998507 -0.998507 \n",
"18 0.085714 0.085714 -1.0 -0.997947 -0.997947 -0.997947 \n",
"\n",
" TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN TGP_MIN TGP_MAX TGP_DIFF \\\n",
"0 -0.997201 -1.0 -0.990854 -0.990854 -0.990854 -0.990854 -1.0 \n",
"4 -0.995428 -1.0 -0.986662 -0.986662 -0.986662 -0.986662 -1.0 \n",
"8 -0.989549 -1.0 -0.956555 -0.956555 -0.956555 -0.956555 -1.0 \n",
"13 -0.998507 -1.0 -0.991235 -0.991235 -0.991235 -0.991235 -1.0 \n",
"18 -0.997947 -1.0 -0.988948 -0.988948 -0.988948 -0.988948 -1.0 \n",
"\n",
" TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX TTPA_DIFF UREA_MEDIAN \\\n",
"0 -0.825613 -0.825613 -0.825613 -0.825613 -1.0 -0.836145 \n",
"4 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 -0.836145 \n",
"8 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 -0.937349 \n",
"13 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 -0.903614 \n",
"18 -0.846633 -0.846633 -0.846633 -0.846633 -1.0 -0.884337 \n",
"\n",
" UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF DIMER_MEDIAN DIMER_MEAN \\\n",
"0 -0.836145 -0.836145 -0.836145 -1.0 -0.994912 -0.994912 \n",
"4 -0.836145 -0.836145 -0.836145 -1.0 -0.978029 -0.978029 \n",
"8 -0.937349 -0.937349 -0.937349 -1.0 -0.978029 -0.978029 \n",
"13 -0.903614 -0.903614 -0.903614 -1.0 -1.000000 -1.000000 \n",
"18 -0.884337 -0.884337 -0.884337 -1.0 -1.000000 -1.000000 \n",
"\n",
" DIMER_MIN DIMER_MAX DIMER_DIFF BLOODPRESSURE_DIASTOLIC_MEAN \\\n",
"0 -0.994912 -0.994912 -1.0 0.086420 \n",
"4 -0.978029 -0.978029 -1.0 -0.551183 \n",
"8 -0.978029 -0.978029 -1.0 -0.132620 \n",
"13 -1.000000 -1.000000 -1.0 0.236838 \n",
"18 -1.000000 -1.000000 -1.0 -0.078189 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEAN HEART_RATE_MEAN RESPIRATORY_RATE_MEAN \\\n",
"0 -0.230769 -0.283019 -0.593220 \n",
"4 -0.757158 -0.005241 -0.683145 \n",
"8 -0.484650 -0.448553 -0.506215 \n",
"13 -0.101680 0.144491 -0.539451 \n",
"18 -0.494587 -0.454228 -0.495292 \n",
"\n",
" TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"0 -0.285714 0.736842 0.086420 \n",
"4 0.357143 0.867507 -0.555556 \n",
"8 -0.119762 0.652398 -0.102881 \n",
"13 0.189142 0.852390 0.251029 \n",
"18 -0.137566 0.835283 -0.090535 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN RESPIRATORY_RATE_MEDIAN \\\n",
"0 -0.230769 -0.283019 -0.586207 \n",
"4 -0.830769 -0.014151 -0.620690 \n",
"8 -0.482051 -0.459119 -0.494253 \n",
"13 -0.102564 0.160377 -0.540230 \n",
"18 -0.492308 -0.449686 -0.494253 \n",
"\n",
" TEMPERATURE_MEDIAN OXYGEN_SATURATION_MEDIAN BLOODPRESSURE_DIASTOLIC_MIN \\\n",
"0 -0.285714 0.736842 0.237113 \n",
"4 0.357143 0.881579 -0.515464 \n",
"8 -0.142857 0.666667 -0.127148 \n",
"13 0.226190 0.833333 0.237113 \n",
"18 -0.136905 0.833333 0.010309 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.000000 -0.162393 -0.500000 \n",
"4 -0.587500 -0.145299 -0.678571 \n",
"8 -0.329167 -0.435897 -0.547619 \n",
"13 0.000000 0.048433 -0.500000 \n",
"18 -0.291667 -0.418803 -0.404762 \n",
"\n",
" TEMPERATURE_MIN OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"0 0.208791 0.898990 -0.247863 \n",
"4 0.604396 0.878788 -0.504274 \n",
"8 0.216117 0.629630 -0.247863 \n",
"13 0.362637 0.925926 -0.076923 \n",
"18 0.252747 0.925926 -0.282051 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"0 -0.459459 -0.432836 -0.636364 \n",
"4 -0.627027 0.000000 -0.636364 \n",
"8 -0.506306 -0.263682 -0.454545 \n",
"13 -0.293694 0.019900 -0.535354 \n",
"18 -0.553153 -0.482587 -0.535354 \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.420290 0.736842 -1.000000 \n",
"4 0.101449 1.000000 -0.626087 \n",
"8 -0.024155 0.754386 -0.692754 \n",
"13 0.072464 0.912281 -0.826087 \n",
"18 -0.246377 0.894737 -0.843478 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"4 -0.613497 -0.572519 -0.852941 \n",
"8 -0.730061 -0.582697 -0.784314 \n",
"13 -0.811861 -0.725191 -0.901961 \n",
"18 -0.820041 -0.821883 -0.980392 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF \\\n",
"0 -1.000000 -1.000000 \n",
"4 -1.000000 -0.878788 \n",
"8 -0.682540 -0.723906 \n",
"13 -0.761905 -0.959596 \n",
"18 -0.904762 -0.966330 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF_REL BLOODPRESSURE_SISTOLIC_DIFF_REL \\\n",
"0 -1.000000 -1.000000 \n",
"4 -0.587195 -0.457543 \n",
"8 -0.769565 -0.685906 \n",
"13 -0.884058 -0.826611 \n",
"18 -0.873174 -0.799242 \n",
"\n",
" HEART_RATE_DIFF_REL RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"4 -0.734417 -0.841577 -1.000000 \n",
"8 -0.689698 -0.776583 -0.682540 \n",
"13 -0.839287 -0.896057 -0.766042 \n",
"18 -0.856110 -0.979689 -0.904177 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"0 -1.000000 0.0 0.0 \n",
"4 -0.881277 1.0 0.0 \n",
"8 -0.724145 0.0 0.0 \n",
"13 -0.960291 1.0 0.0 \n",
"18 -0.967019 1.0 0.0 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"8 0.0 1.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"0 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th ICU \n",
"0 0.0 0.0 1.0 \n",
"4 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 "
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 338
},
"id": "tKo_cr_32XPT",
"outputId": "e259949b-7f71-4acf-ad48-55e2a60a71af"
},
"source": [
"final_data.describe()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>352.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>351.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.000000</td>\n",
" <td>334.0</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>315.000000</td>\n",
" <td>315.000000</td>\n",
" <td>316.000000</td>\n",
" <td>306.000000</td>\n",
" <td>313.000000</td>\n",
" <td>316.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" <td>352.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.457386</td>\n",
" <td>0.105413</td>\n",
" <td>0.022792</td>\n",
" <td>0.091168</td>\n",
" <td>0.019943</td>\n",
" <td>0.128205</td>\n",
" <td>0.042735</td>\n",
" <td>0.193732</td>\n",
" <td>0.162393</td>\n",
" <td>0.831909</td>\n",
" <td>0.577424</td>\n",
" <td>0.577424</td>\n",
" <td>0.577424</td>\n",
" <td>0.577424</td>\n",
" <td>-1.0</td>\n",
" <td>-0.998666</td>\n",
" <td>-0.998666</td>\n",
" <td>-0.998666</td>\n",
" <td>-0.998666</td>\n",
" <td>-1.0</td>\n",
" <td>-0.967693</td>\n",
" <td>-0.967693</td>\n",
" <td>-0.967693</td>\n",
" <td>-0.967693</td>\n",
" <td>-1.0</td>\n",
" <td>-0.319167</td>\n",
" <td>-0.319167</td>\n",
" <td>-0.319167</td>\n",
" <td>-0.319167</td>\n",
" <td>-1.0</td>\n",
" <td>-0.323864</td>\n",
" <td>-0.323864</td>\n",
" <td>-0.323864</td>\n",
" <td>-0.323864</td>\n",
" <td>-1.0</td>\n",
" <td>-0.945793</td>\n",
" <td>-0.945793</td>\n",
" <td>-0.945793</td>\n",
" <td>-0.945793</td>\n",
" <td>-1.0</td>\n",
" <td>-0.993232</td>\n",
" <td>-0.993232</td>\n",
" <td>-0.993232</td>\n",
" <td>-0.993232</td>\n",
" <td>-1.0</td>\n",
" <td>0.330115</td>\n",
" <td>0.330115</td>\n",
" <td>0.330115</td>\n",
" <td>0.330115</td>\n",
" <td>-1.0</td>\n",
" <td>-0.893221</td>\n",
" <td>-0.893221</td>\n",
" <td>-0.893221</td>\n",
" <td>-0.893221</td>\n",
" <td>-1.0</td>\n",
" <td>-0.726559</td>\n",
" <td>-0.726559</td>\n",
" <td>-0.726559</td>\n",
" <td>-0.726559</td>\n",
" <td>-1.0</td>\n",
" <td>-0.932968</td>\n",
" <td>-0.932968</td>\n",
" <td>-0.932968</td>\n",
" <td>-0.932968</td>\n",
" <td>-1.0</td>\n",
" <td>-0.851721</td>\n",
" <td>-0.851721</td>\n",
" <td>-0.851721</td>\n",
" <td>-0.851721</td>\n",
" <td>-1.0</td>\n",
" <td>-0.111532</td>\n",
" <td>-0.111532</td>\n",
" <td>-0.111532</td>\n",
" <td>-0.111532</td>\n",
" <td>-1.0</td>\n",
" <td>-0.146346</td>\n",
" <td>-0.146346</td>\n",
" <td>-0.146346</td>\n",
" <td>-0.146346</td>\n",
" <td>-1.0</td>\n",
" <td>-0.941465</td>\n",
" <td>-0.941465</td>\n",
" <td>-0.941465</td>\n",
" <td>-0.941465</td>\n",
" <td>-1.0</td>\n",
" <td>0.563159</td>\n",
" <td>0.563159</td>\n",
" <td>0.563159</td>\n",
" <td>0.563159</td>\n",
" <td>-1.0</td>\n",
" <td>-0.770982</td>\n",
" <td>-0.770982</td>\n",
" <td>-0.770982</td>\n",
" <td>-0.770982</td>\n",
" <td>-1.0</td>\n",
" <td>-0.747070</td>\n",
" <td>-0.747070</td>\n",
" <td>-0.747070</td>\n",
" <td>-0.747070</td>\n",
" <td>-1.0</td>\n",
" <td>-0.833163</td>\n",
" <td>-0.833163</td>\n",
" <td>-0.833163</td>\n",
" <td>-0.833163</td>\n",
" <td>-1.0</td>\n",
" <td>-0.175150</td>\n",
" <td>-0.175150</td>\n",
" <td>-0.175150</td>\n",
" <td>-0.175150</td>\n",
" <td>-1.0</td>\n",
" <td>-0.696152</td>\n",
" <td>-0.696152</td>\n",
" <td>-0.696152</td>\n",
" <td>-0.696152</td>\n",
" <td>-1.0</td>\n",
" <td>-0.780171</td>\n",
" <td>-0.780171</td>\n",
" <td>-0.780171</td>\n",
" <td>-0.780171</td>\n",
" <td>-1.0</td>\n",
" <td>-0.765781</td>\n",
" <td>-0.765781</td>\n",
" <td>-0.765781</td>\n",
" <td>-0.765781</td>\n",
" <td>-1.0</td>\n",
" <td>-0.818596</td>\n",
" <td>-0.818596</td>\n",
" <td>-0.818596</td>\n",
" <td>-0.818596</td>\n",
" <td>-1.0</td>\n",
" <td>0.232875</td>\n",
" <td>0.232875</td>\n",
" <td>0.232875</td>\n",
" <td>0.232875</td>\n",
" <td>-1.0</td>\n",
" <td>0.379952</td>\n",
" <td>0.379952</td>\n",
" <td>0.379952</td>\n",
" <td>0.379952</td>\n",
" <td>-1.0</td>\n",
" <td>-0.501035</td>\n",
" <td>-0.501035</td>\n",
" <td>-0.501035</td>\n",
" <td>-0.501035</td>\n",
" <td>-1.0</td>\n",
" <td>-0.558346</td>\n",
" <td>-0.558346</td>\n",
" <td>-0.558346</td>\n",
" <td>-0.558346</td>\n",
" <td>-1.0</td>\n",
" <td>0.928083</td>\n",
" <td>0.928083</td>\n",
" <td>0.928083</td>\n",
" <td>0.928083</td>\n",
" <td>-1.0</td>\n",
" <td>0.299087</td>\n",
" <td>0.299087</td>\n",
" <td>0.299087</td>\n",
" <td>0.299087</td>\n",
" <td>-1.0</td>\n",
" <td>-0.090048</td>\n",
" <td>-0.090048</td>\n",
" <td>-0.090048</td>\n",
" <td>-0.090048</td>\n",
" <td>-1.0</td>\n",
" <td>-0.993567</td>\n",
" <td>-0.993567</td>\n",
" <td>-0.993567</td>\n",
" <td>-0.993567</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986554</td>\n",
" <td>-0.986554</td>\n",
" <td>-0.986554</td>\n",
" <td>-0.986554</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836616</td>\n",
" <td>-0.836616</td>\n",
" <td>-0.836616</td>\n",
" <td>-0.836616</td>\n",
" <td>-1.0</td>\n",
" <td>-0.852589</td>\n",
" <td>-0.852589</td>\n",
" <td>-0.852589</td>\n",
" <td>-0.852589</td>\n",
" <td>-1.0</td>\n",
" <td>-0.953361</td>\n",
" <td>-0.953361</td>\n",
" <td>-0.953361</td>\n",
" <td>-0.953361</td>\n",
" <td>-1.0</td>\n",
" <td>-0.042859</td>\n",
" <td>-0.339638</td>\n",
" <td>-0.246726</td>\n",
" <td>-0.496321</td>\n",
" <td>0.104692</td>\n",
" <td>0.740155</td>\n",
" <td>-0.043791</td>\n",
" <td>-0.341907</td>\n",
" <td>-0.245776</td>\n",
" <td>-0.488741</td>\n",
" <td>0.101438</td>\n",
" <td>0.743768</td>\n",
" <td>0.067534</td>\n",
" <td>-0.137126</td>\n",
" <td>-0.188963</td>\n",
" <td>-0.443044</td>\n",
" <td>0.399847</td>\n",
" <td>0.871122</td>\n",
" <td>-0.278547</td>\n",
" <td>-0.484893</td>\n",
" <td>-0.344516</td>\n",
" <td>-0.502129</td>\n",
" <td>-0.028106</td>\n",
" <td>0.781299</td>\n",
" <td>-0.888180</td>\n",
" <td>-0.894264</td>\n",
" <td>-0.885927</td>\n",
" <td>-0.916619</td>\n",
" <td>-0.884826</td>\n",
" <td>-0.955068</td>\n",
" <td>-0.909164</td>\n",
" <td>-0.886210</td>\n",
" <td>-0.916102</td>\n",
" <td>-0.914525</td>\n",
" <td>-0.885639</td>\n",
" <td>-0.955110</td>\n",
" <td>0.107955</td>\n",
" <td>0.119318</td>\n",
" <td>0.110795</td>\n",
" <td>0.107955</td>\n",
" <td>0.093750</td>\n",
" <td>0.085227</td>\n",
" <td>0.096591</td>\n",
" <td>0.102273</td>\n",
" <td>0.079545</td>\n",
" <td>0.096591</td>\n",
" <td>0.463068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.498890</td>\n",
" <td>0.307523</td>\n",
" <td>0.149453</td>\n",
" <td>0.288259</td>\n",
" <td>0.140004</td>\n",
" <td>0.334795</td>\n",
" <td>0.202548</td>\n",
" <td>0.395786</td>\n",
" <td>0.369338</td>\n",
" <td>0.374481</td>\n",
" <td>0.121547</td>\n",
" <td>0.121547</td>\n",
" <td>0.121547</td>\n",
" <td>0.121547</td>\n",
" <td>0.0</td>\n",
" <td>0.013419</td>\n",
" <td>0.013419</td>\n",
" <td>0.013419</td>\n",
" <td>0.013419</td>\n",
" <td>0.0</td>\n",
" <td>0.096084</td>\n",
" <td>0.096084</td>\n",
" <td>0.096084</td>\n",
" <td>0.096084</td>\n",
" <td>0.0</td>\n",
" <td>0.031116</td>\n",
" <td>0.031116</td>\n",
" <td>0.031116</td>\n",
" <td>0.031116</td>\n",
" <td>0.0</td>\n",
" <td>0.082815</td>\n",
" <td>0.082815</td>\n",
" <td>0.082815</td>\n",
" <td>0.082815</td>\n",
" <td>0.0</td>\n",
" <td>0.043791</td>\n",
" <td>0.043791</td>\n",
" <td>0.043791</td>\n",
" <td>0.043791</td>\n",
" <td>0.0</td>\n",
" <td>0.110317</td>\n",
" <td>0.110317</td>\n",
" <td>0.110317</td>\n",
" <td>0.110317</td>\n",
" <td>0.0</td>\n",
" <td>0.079810</td>\n",
" <td>0.079810</td>\n",
" <td>0.079810</td>\n",
" <td>0.079810</td>\n",
" <td>0.0</td>\n",
" <td>0.097262</td>\n",
" <td>0.097262</td>\n",
" <td>0.097262</td>\n",
" <td>0.097262</td>\n",
" <td>0.0</td>\n",
" <td>0.173660</td>\n",
" <td>0.173660</td>\n",
" <td>0.173660</td>\n",
" <td>0.173660</td>\n",
" <td>0.0</td>\n",
" <td>0.136750</td>\n",
" <td>0.136750</td>\n",
" <td>0.136750</td>\n",
" <td>0.136750</td>\n",
" <td>0.0</td>\n",
" <td>0.117084</td>\n",
" <td>0.117084</td>\n",
" <td>0.117084</td>\n",
" <td>0.117084</td>\n",
" <td>0.0</td>\n",
" <td>0.212111</td>\n",
" <td>0.212111</td>\n",
" <td>0.212111</td>\n",
" <td>0.212111</td>\n",
" <td>0.0</td>\n",
" <td>0.223863</td>\n",
" <td>0.223863</td>\n",
" <td>0.223863</td>\n",
" <td>0.223863</td>\n",
" <td>0.0</td>\n",
" <td>0.054453</td>\n",
" <td>0.054453</td>\n",
" <td>0.054453</td>\n",
" <td>0.054453</td>\n",
" <td>0.0</td>\n",
" <td>0.744763</td>\n",
" <td>0.744763</td>\n",
" <td>0.744763</td>\n",
" <td>0.744763</td>\n",
" <td>0.0</td>\n",
" <td>0.115200</td>\n",
" <td>0.115200</td>\n",
" <td>0.115200</td>\n",
" <td>0.115200</td>\n",
" <td>0.0</td>\n",
" <td>0.141870</td>\n",
" <td>0.141870</td>\n",
" <td>0.141870</td>\n",
" <td>0.141870</td>\n",
" <td>0.0</td>\n",
" <td>0.110799</td>\n",
" <td>0.110799</td>\n",
" <td>0.110799</td>\n",
" <td>0.110799</td>\n",
" <td>0.0</td>\n",
" <td>0.070195</td>\n",
" <td>0.070195</td>\n",
" <td>0.070195</td>\n",
" <td>0.070195</td>\n",
" <td>0.0</td>\n",
" <td>0.128496</td>\n",
" <td>0.128496</td>\n",
" <td>0.128496</td>\n",
" <td>0.128496</td>\n",
" <td>0.0</td>\n",
" <td>0.010824</td>\n",
" <td>0.010824</td>\n",
" <td>0.010824</td>\n",
" <td>0.010824</td>\n",
" <td>0.0</td>\n",
" <td>0.049559</td>\n",
" <td>0.049559</td>\n",
" <td>0.049559</td>\n",
" <td>0.049559</td>\n",
" <td>0.0</td>\n",
" <td>0.222292</td>\n",
" <td>0.222292</td>\n",
" <td>0.222292</td>\n",
" <td>0.222292</td>\n",
" <td>0.0</td>\n",
" <td>0.044128</td>\n",
" <td>0.044128</td>\n",
" <td>0.044128</td>\n",
" <td>0.044128</td>\n",
" <td>0.0</td>\n",
" <td>0.090438</td>\n",
" <td>0.090438</td>\n",
" <td>0.090438</td>\n",
" <td>0.090438</td>\n",
" <td>0.0</td>\n",
" <td>0.188765</td>\n",
" <td>0.188765</td>\n",
" <td>0.188765</td>\n",
" <td>0.188765</td>\n",
" <td>0.0</td>\n",
" <td>0.157769</td>\n",
" <td>0.157769</td>\n",
" <td>0.157769</td>\n",
" <td>0.157769</td>\n",
" <td>0.0</td>\n",
" <td>0.135022</td>\n",
" <td>0.135022</td>\n",
" <td>0.135022</td>\n",
" <td>0.135022</td>\n",
" <td>0.0</td>\n",
" <td>0.300392</td>\n",
" <td>0.300392</td>\n",
" <td>0.300392</td>\n",
" <td>0.300392</td>\n",
" <td>0.0</td>\n",
" <td>0.202813</td>\n",
" <td>0.202813</td>\n",
" <td>0.202813</td>\n",
" <td>0.202813</td>\n",
" <td>0.0</td>\n",
" <td>0.017891</td>\n",
" <td>0.017891</td>\n",
" <td>0.017891</td>\n",
" <td>0.017891</td>\n",
" <td>0.0</td>\n",
" <td>0.015050</td>\n",
" <td>0.015050</td>\n",
" <td>0.015050</td>\n",
" <td>0.015050</td>\n",
" <td>0.0</td>\n",
" <td>0.043978</td>\n",
" <td>0.043978</td>\n",
" <td>0.043978</td>\n",
" <td>0.043978</td>\n",
" <td>0.0</td>\n",
" <td>0.074589</td>\n",
" <td>0.074589</td>\n",
" <td>0.074589</td>\n",
" <td>0.074589</td>\n",
" <td>0.0</td>\n",
" <td>0.131882</td>\n",
" <td>0.131882</td>\n",
" <td>0.131882</td>\n",
" <td>0.131882</td>\n",
" <td>0.0</td>\n",
" <td>0.236802</td>\n",
" <td>0.264567</td>\n",
" <td>0.257633</td>\n",
" <td>0.176096</td>\n",
" <td>0.237660</td>\n",
" <td>0.156718</td>\n",
" <td>0.239481</td>\n",
" <td>0.265640</td>\n",
" <td>0.266321</td>\n",
" <td>0.180155</td>\n",
" <td>0.240952</td>\n",
" <td>0.159115</td>\n",
" <td>0.207192</td>\n",
" <td>0.225559</td>\n",
" <td>0.242153</td>\n",
" <td>0.194649</td>\n",
" <td>0.160015</td>\n",
" <td>0.101621</td>\n",
" <td>0.184857</td>\n",
" <td>0.192835</td>\n",
" <td>0.213527</td>\n",
" <td>0.171294</td>\n",
" <td>0.209891</td>\n",
" <td>0.159679</td>\n",
" <td>0.138282</td>\n",
" <td>0.127159</td>\n",
" <td>0.139345</td>\n",
" <td>0.104626</td>\n",
" <td>0.134078</td>\n",
" <td>0.092444</td>\n",
" <td>0.114055</td>\n",
" <td>0.135249</td>\n",
" <td>0.097268</td>\n",
" <td>0.105600</td>\n",
" <td>0.132334</td>\n",
" <td>0.092310</td>\n",
" <td>0.310765</td>\n",
" <td>0.324624</td>\n",
" <td>0.314325</td>\n",
" <td>0.310765</td>\n",
" <td>0.291896</td>\n",
" <td>0.279617</td>\n",
" <td>0.295820</td>\n",
" <td>0.303438</td>\n",
" <td>0.270973</td>\n",
" <td>0.295820</td>\n",
" <td>0.499344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.578947</td>\n",
" <td>-0.578947</td>\n",
" <td>-0.578947</td>\n",
" <td>-0.578947</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.756098</td>\n",
" <td>-0.756098</td>\n",
" <td>-0.756098</td>\n",
" <td>-0.756098</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994767</td>\n",
" <td>-0.994767</td>\n",
" <td>-0.994767</td>\n",
" <td>-0.994767</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.970276</td>\n",
" <td>-0.970276</td>\n",
" <td>-0.970276</td>\n",
" <td>-0.970276</td>\n",
" <td>-1.0</td>\n",
" <td>-0.927505</td>\n",
" <td>-0.927505</td>\n",
" <td>-0.927505</td>\n",
" <td>-0.927505</td>\n",
" <td>-1.0</td>\n",
" <td>-0.997664</td>\n",
" <td>-0.997664</td>\n",
" <td>-0.997664</td>\n",
" <td>-0.997664</td>\n",
" <td>-1.0</td>\n",
" <td>-0.929236</td>\n",
" <td>-0.929236</td>\n",
" <td>-0.929236</td>\n",
" <td>-0.929236</td>\n",
" <td>-1.0</td>\n",
" <td>-0.903564</td>\n",
" <td>-0.903564</td>\n",
" <td>-0.903564</td>\n",
" <td>-0.903564</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.975884</td>\n",
" <td>-0.975884</td>\n",
" <td>-0.975884</td>\n",
" <td>-0.975884</td>\n",
" <td>-1.0</td>\n",
" <td>-0.966010</td>\n",
" <td>-0.966010</td>\n",
" <td>-0.966010</td>\n",
" <td>-0.966010</td>\n",
" <td>-1.0</td>\n",
" <td>-0.977178</td>\n",
" <td>-0.977178</td>\n",
" <td>-0.977178</td>\n",
" <td>-0.977178</td>\n",
" <td>-1.0</td>\n",
" <td>-0.990796</td>\n",
" <td>-0.990796</td>\n",
" <td>-0.990796</td>\n",
" <td>-0.990796</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.988166</td>\n",
" <td>-0.988166</td>\n",
" <td>-0.988166</td>\n",
" <td>-0.988166</td>\n",
" <td>-1.0</td>\n",
" <td>-0.958621</td>\n",
" <td>-0.958621</td>\n",
" <td>-0.958621</td>\n",
" <td>-0.958621</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.489362</td>\n",
" <td>-0.489362</td>\n",
" <td>-0.489362</td>\n",
" <td>-0.489362</td>\n",
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" <td>-0.090909</td>\n",
" <td>-0.090909</td>\n",
" <td>-0.090909</td>\n",
" <td>-0.090909</td>\n",
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" <td>-0.991989</td>\n",
" <td>-0.991989</td>\n",
" <td>-0.991989</td>\n",
" <td>-0.991989</td>\n",
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" <td>-0.962963</td>\n",
" <td>-0.962963</td>\n",
" <td>-0.962963</td>\n",
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" <td>-0.925926</td>\n",
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" <td>-0.925926</td>\n",
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" <td>-0.771429</td>\n",
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" <td>-0.771429</td>\n",
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" <td>-0.999627</td>\n",
" <td>-0.999627</td>\n",
" <td>-0.999627</td>\n",
" <td>-0.999627</td>\n",
" <td>-1.0</td>\n",
" <td>-0.999619</td>\n",
" <td>-0.999619</td>\n",
" <td>-0.999619</td>\n",
" <td>-0.999619</td>\n",
" <td>-1.0</td>\n",
" <td>-0.961853</td>\n",
" <td>-0.961853</td>\n",
" <td>-0.961853</td>\n",
" <td>-0.961853</td>\n",
" <td>-1.0</td>\n",
" <td>-0.978313</td>\n",
" <td>-0.978313</td>\n",
" <td>-0.978313</td>\n",
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" <td>-0.654321</td>\n",
" <td>-0.880959</td>\n",
" <td>-0.830189</td>\n",
" <td>-0.932203</td>\n",
" <td>-0.500000</td>\n",
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" <td>-0.679012</td>\n",
" <td>-0.884615</td>\n",
" <td>-0.830189</td>\n",
" <td>-0.931034</td>\n",
" <td>-0.500000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.587500</td>\n",
" <td>-0.803419</td>\n",
" <td>-0.857143</td>\n",
" <td>0.054945</td>\n",
" <td>0.040404</td>\n",
" <td>-0.760684</td>\n",
" <td>-0.891892</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>0.605263</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
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" <td>-1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-1.000000</td>\n",
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" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-0.317073</td>\n",
" <td>-1.0</td>\n",
" <td>-0.963370</td>\n",
" <td>-0.963370</td>\n",
" <td>-0.963370</td>\n",
" <td>-0.963370</td>\n",
" <td>-1.0</td>\n",
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" <td>-1.000000</td>\n",
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" <td>-1.0</td>\n",
" <td>0.331633</td>\n",
" <td>0.331633</td>\n",
" <td>0.331633</td>\n",
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" <td>-0.927813</td>\n",
" <td>-0.927813</td>\n",
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" <tr>\n",
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" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.754601</td>\n",
" <td>-1.0</td>\n",
" <td>-0.753403</td>\n",
" <td>-0.753403</td>\n",
" <td>-0.753403</td>\n",
" <td>-0.753403</td>\n",
" <td>-1.0</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>0.234043</td>\n",
" <td>-1.0</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>0.363636</td>\n",
" <td>-1.0</td>\n",
" <td>-0.405207</td>\n",
" <td>-0.405207</td>\n",
" <td>-0.405207</td>\n",
" <td>-0.405207</td>\n",
" <td>-1.0</td>\n",
" <td>-0.481481</td>\n",
" <td>-0.481481</td>\n",
" <td>-0.481481</td>\n",
" <td>-0.481481</td>\n",
" <td>-1.0</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>0.047619</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-0.986662</td>\n",
" <td>-1.0</td>\n",
" <td>-0.837096</td>\n",
" <td>-0.837096</td>\n",
" <td>-0.837096</td>\n",
" <td>-0.837096</td>\n",
" <td>-1.0</td>\n",
" <td>-0.826506</td>\n",
" <td>-0.826506</td>\n",
" <td>-0.826506</td>\n",
" <td>-0.826506</td>\n",
" <td>-1.0</td>\n",
" <td>-0.964345</td>\n",
" <td>-0.964345</td>\n",
" <td>-0.964345</td>\n",
" <td>-0.964345</td>\n",
" <td>-1.0</td>\n",
" <td>0.086420</td>\n",
" <td>-0.215385</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.457627</td>\n",
" <td>0.250000</td>\n",
" <td>0.831236</td>\n",
" <td>0.086420</td>\n",
" <td>-0.225641</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.448276</td>\n",
" <td>0.250000</td>\n",
" <td>0.842105</td>\n",
" <td>0.195876</td>\n",
" <td>-0.031250</td>\n",
" <td>-0.089744</td>\n",
" <td>-0.404762</td>\n",
" <td>0.494505</td>\n",
" <td>0.919192</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.362162</td>\n",
" <td>-0.238806</td>\n",
" <td>-0.454545</td>\n",
" <td>0.101449</td>\n",
" <td>0.870614</td>\n",
" <td>-0.798551</td>\n",
" <td>-0.810838</td>\n",
" <td>-0.793893</td>\n",
" <td>-0.862745</td>\n",
" <td>-0.793651</td>\n",
" <td>-0.944444</td>\n",
" <td>-0.836440</td>\n",
" <td>-0.791275</td>\n",
" <td>-0.849462</td>\n",
" <td>-0.857826</td>\n",
" <td>-0.795906</td>\n",
" <td>-0.944811</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-1.0</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-1.0</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-1.0</td>\n",
" <td>0.024390</td>\n",
" <td>0.024390</td>\n",
" <td>0.024390</td>\n",
" <td>0.024390</td>\n",
" <td>-1.0</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>-1.0</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.452514</td>\n",
" <td>0.452514</td>\n",
" <td>0.452514</td>\n",
" <td>0.452514</td>\n",
" <td>-1.0</td>\n",
" <td>0.811321</td>\n",
" <td>0.811321</td>\n",
" <td>0.811321</td>\n",
" <td>0.811321</td>\n",
" <td>-1.0</td>\n",
" <td>0.804878</td>\n",
" <td>0.804878</td>\n",
" <td>0.804878</td>\n",
" <td>0.804878</td>\n",
" <td>-1.0</td>\n",
" <td>-0.355082</td>\n",
" <td>-0.355082</td>\n",
" <td>-0.355082</td>\n",
" <td>-0.355082</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-1.0</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>-1.0</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-1.0</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>-1.0</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>-1.0</td>\n",
" <td>-0.737931</td>\n",
" <td>-0.737931</td>\n",
" <td>-0.737931</td>\n",
" <td>-0.737931</td>\n",
" <td>-1.0</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-1.0</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>-1.0</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>-1.0</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>-1.0</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-1.0</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-1.0</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-1.0</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.905660</td>\n",
" <td>0.830508</td>\n",
" <td>0.964286</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.962264</td>\n",
" <td>0.862069</td>\n",
" <td>0.964286</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.914530</td>\n",
" <td>1.000000</td>\n",
" <td>0.978022</td>\n",
" <td>1.000000</td>\n",
" <td>0.384615</td>\n",
" <td>0.405405</td>\n",
" <td>0.552239</td>\n",
" <td>0.636364</td>\n",
" <td>0.768116</td>\n",
" <td>1.000000</td>\n",
" <td>-0.217391</td>\n",
" <td>-0.447853</td>\n",
" <td>0.160305</td>\n",
" <td>-0.323529</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.090909</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.456376</td>\n",
" <td>-0.531749</td>\n",
" <td>-0.299283</td>\n",
" <td>-0.285468</td>\n",
" <td>-0.091698</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 1 DISEASE GROUPING 2 \\\n",
"count 352.000000 351.000000 351.000000 \n",
"mean 0.457386 0.105413 0.022792 \n",
"std 0.498890 0.307523 0.149453 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 1.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" DISEASE GROUPING 3 DISEASE GROUPING 4 DISEASE GROUPING 5 \\\n",
"count 351.000000 351.000000 351.000000 \n",
"mean 0.091168 0.019943 0.128205 \n",
"std 0.288259 0.140004 0.334795 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"count 351.000000 351.000000 351.000000 351.000000 \n",
"mean 0.042735 0.193732 0.162393 0.831909 \n",
"std 0.202548 0.395786 0.369338 0.374481 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 1.000000 \n",
"50% 0.000000 0.000000 0.000000 1.000000 \n",
"75% 0.000000 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.000000 334.0 \n",
"mean 0.577424 0.577424 0.577424 0.577424 -1.0 \n",
"std 0.121547 0.121547 0.121547 0.121547 0.0 \n",
"min -0.578947 -0.578947 -0.578947 -0.578947 -1.0 \n",
"25% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"50% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"75% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"max 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.998666 -0.998666 -0.998666 -0.998666 \n",
"std 0.013419 0.013419 0.013419 0.013419 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"50% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"75% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"max -0.786096 -0.786096 -0.786096 -0.786096 \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"count 334.0 334.000000 334.000000 334.000000 \n",
"mean -1.0 -0.967693 -0.967693 -0.967693 \n",
"std 0.0 0.096084 0.096084 0.096084 \n",
"min -1.0 -1.000000 -1.000000 -1.000000 \n",
"25% -1.0 -1.000000 -1.000000 -1.000000 \n",
"50% -1.0 -1.000000 -1.000000 -1.000000 \n",
"75% -1.0 -1.000000 -1.000000 -1.000000 \n",
"max -1.0 -0.204188 -0.204188 -0.204188 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"count 334.000000 334.0 334.000000 334.000000 \n",
"mean -0.967693 -1.0 -0.319167 -0.319167 \n",
"std 0.096084 0.0 0.031116 0.031116 \n",
"min -1.000000 -1.0 -0.756098 -0.756098 \n",
"25% -1.000000 -1.0 -0.317073 -0.317073 \n",
"50% -1.000000 -1.0 -0.317073 -0.317073 \n",
"75% -1.000000 -1.0 -0.317073 -0.317073 \n",
"max -0.204188 -1.0 -0.219512 -0.219512 \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF \\\n",
"count 334.000000 334.000000 334.0 \n",
"mean -0.319167 -0.319167 -1.0 \n",
"std 0.031116 0.031116 0.0 \n",
"min -0.756098 -0.756098 -1.0 \n",
"25% -0.317073 -0.317073 -1.0 \n",
"50% -0.317073 -0.317073 -1.0 \n",
"75% -0.317073 -0.317073 -1.0 \n",
"max -0.219512 -0.219512 -1.0 \n",
"\n",
" BIC_VENOUS_MEDIAN BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.323864 -0.323864 -0.323864 -0.323864 \n",
"std 0.082815 0.082815 0.082815 0.082815 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"50% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"75% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"max 0.024390 0.024390 0.024390 0.024390 \n",
"\n",
" BIC_VENOUS_DIFF BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN \\\n",
"count 334.0 334.000000 334.000000 334.000000 \n",
"mean -1.0 -0.945793 -0.945793 -0.945793 \n",
"std 0.0 0.043791 0.043791 0.043791 \n",
"min -1.0 -0.994767 -0.994767 -0.994767 \n",
"25% -1.0 -0.963370 -0.963370 -0.963370 \n",
"50% -1.0 -0.938950 -0.938950 -0.938950 \n",
"75% -1.0 -0.938950 -0.938950 -0.938950 \n",
"max -1.0 -0.293564 -0.293564 -0.293564 \n",
"\n",
" BILLIRUBIN_MAX BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN \\\n",
"count 334.000000 334.0 334.000000 334.000000 334.000000 \n",
"mean -0.945793 -1.0 -0.993232 -0.993232 -0.993232 \n",
"std 0.043791 0.0 0.110317 0.110317 0.110317 \n",
"min -0.994767 -1.0 -1.000000 -1.000000 -1.000000 \n",
"25% -0.963370 -1.0 -1.000000 -1.000000 -1.000000 \n",
"50% -0.938950 -1.0 -1.000000 -1.000000 -1.000000 \n",
"75% -0.938950 -1.0 -1.000000 -1.000000 -1.000000 \n",
"max -0.293564 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" BLAST_MAX BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN \\\n",
"count 334.000000 334.0 334.000000 334.000000 334.000000 \n",
"mean -0.993232 -1.0 0.330115 0.330115 0.330115 \n",
"std 0.110317 0.0 0.079810 0.079810 0.079810 \n",
"min -1.000000 -1.0 0.000000 0.000000 0.000000 \n",
"25% -1.000000 -1.0 0.331633 0.331633 0.331633 \n",
"50% -1.000000 -1.0 0.357143 0.357143 0.357143 \n",
"75% -1.000000 -1.0 0.357143 0.357143 0.357143 \n",
"max 1.000000 -1.0 0.693878 0.693878 0.693878 \n",
"\n",
" CALCIUM_MAX CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN \\\n",
"count 334.000000 334.0 334.000000 334.000000 \n",
"mean 0.330115 -1.0 -0.893221 -0.893221 \n",
"std 0.079810 0.0 0.097262 0.097262 \n",
"min 0.000000 -1.0 -0.970276 -0.970276 \n",
"25% 0.331633 -1.0 -0.927813 -0.927813 \n",
"50% 0.357143 -1.0 -0.908553 -0.908553 \n",
"75% 0.357143 -1.0 -0.882696 -0.882696 \n",
"max 0.693878 -1.0 0.493277 0.493277 \n",
"\n",
" CREATININ_MIN CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN \\\n",
"count 334.000000 334.000000 334.0 334.000000 334.000000 \n",
"mean -0.893221 -0.893221 -1.0 -0.726559 -0.726559 \n",
"std 0.097262 0.097262 0.0 0.173660 0.173660 \n",
"min -0.970276 -0.970276 -1.0 -0.927505 -0.927505 \n",
"25% -0.927813 -0.927813 -1.0 -0.747335 -0.747335 \n",
"50% -0.908553 -0.908553 -1.0 -0.742004 -0.742004 \n",
"75% -0.882696 -0.882696 -1.0 -0.742004 -0.742004 \n",
"max 0.493277 0.493277 -1.0 1.000000 1.000000 \n",
"\n",
" FFA_MIN FFA_MAX FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN \\\n",
"count 334.000000 334.000000 334.0 334.000000 334.000000 334.000000 \n",
"mean -0.726559 -0.726559 -1.0 -0.932968 -0.932968 -0.932968 \n",
"std 0.173660 0.173660 0.0 0.136750 0.136750 0.136750 \n",
"min -0.927505 -0.927505 -1.0 -0.997664 -0.997664 -0.997664 \n",
"25% -0.747335 -0.747335 -1.0 -0.958528 -0.958528 -0.958528 \n",
"50% -0.742004 -0.742004 -1.0 -0.958528 -0.958528 -0.958528 \n",
"75% -0.742004 -0.742004 -1.0 -0.958528 -0.958528 -0.958528 \n",
"max 1.000000 1.000000 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" GGT_MAX GGT_DIFF GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN \\\n",
"count 334.000000 334.0 334.000000 334.000000 334.000000 \n",
"mean -0.932968 -1.0 -0.851721 -0.851721 -0.851721 \n",
"std 0.136750 0.0 0.117084 0.117084 0.117084 \n",
"min -0.997664 -1.0 -0.929236 -0.929236 -0.929236 \n",
"25% -0.958528 -1.0 -0.891993 -0.891993 -0.891993 \n",
"50% -0.958528 -1.0 -0.891993 -0.891993 -0.891993 \n",
"75% -0.958528 -1.0 -0.858473 -0.858473 -0.858473 \n",
"max 1.000000 -1.0 0.452514 0.452514 0.452514 \n",
"\n",
" GLUCOSE_MAX GLUCOSE_DIFF HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN \\\n",
"count 334.000000 334.0 334.000000 334.000000 \n",
"mean -0.851721 -1.0 -0.111532 -0.111532 \n",
"std 0.117084 0.0 0.212111 0.212111 \n",
"min -0.929236 -1.0 -0.903564 -0.903564 \n",
"25% -0.891993 -1.0 -0.227463 -0.227463 \n",
"50% -0.891993 -1.0 -0.102725 -0.102725 \n",
"75% -0.858473 -1.0 0.023061 0.023061 \n",
"max 0.452514 -1.0 0.811321 0.811321 \n",
"\n",
" HEMATOCRITE_MIN HEMATOCRITE_MAX HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN \\\n",
"count 334.000000 334.000000 334.0 334.000000 \n",
"mean -0.111532 -0.111532 -1.0 -0.146346 \n",
"std 0.212111 0.212111 0.0 0.223863 \n",
"min -0.903564 -0.903564 -1.0 -1.000000 \n",
"25% -0.227463 -0.227463 -1.0 -0.268293 \n",
"50% -0.102725 -0.102725 -1.0 -0.140244 \n",
"75% 0.023061 0.023061 -1.0 0.009146 \n",
"max 0.811321 0.811321 -1.0 0.804878 \n",
"\n",
" HEMOGLOBIN_MEAN HEMOGLOBIN_MIN HEMOGLOBIN_MAX HEMOGLOBIN_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.0 \n",
"mean -0.146346 -0.146346 -0.146346 -1.0 \n",
"std 0.223863 0.223863 0.223863 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.268293 -0.268293 -0.268293 -1.0 \n",
"50% -0.140244 -0.140244 -0.140244 -1.0 \n",
"75% 0.009146 0.009146 0.009146 -1.0 \n",
"max 0.804878 0.804878 0.804878 -1.0 \n",
"\n",
" INR_MEDIAN INR_MEAN INR_MIN INR_MAX INR_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.000000 334.0 \n",
"mean -0.941465 -0.941465 -0.941465 -0.941465 -1.0 \n",
"std 0.054453 0.054453 0.054453 0.054453 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"50% -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"75% -0.933501 -0.933501 -0.933501 -0.933501 -1.0 \n",
"max -0.355082 -0.355082 -0.355082 -0.355082 -1.0 \n",
"\n",
" LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX LACTATE_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.000000 334.0 \n",
"mean 0.563159 0.563159 0.563159 0.563159 -1.0 \n",
"std 0.744763 0.744763 0.744763 0.744763 0.0 \n",
"min -0.975884 -0.975884 -0.975884 -0.975884 -1.0 \n",
"25% 0.062672 0.062672 0.062672 0.062672 -1.0 \n",
"50% 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"75% 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"max 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN LEUKOCYTES_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.770982 -0.770982 -0.770982 -0.770982 \n",
"std 0.115200 0.115200 0.115200 0.115200 \n",
"min -0.966010 -0.966010 -0.966010 -0.966010 \n",
"25% -0.842410 -0.842410 -0.842410 -0.842410 \n",
"50% -0.798764 -0.798764 -0.798764 -0.798764 \n",
"75% -0.729287 -0.729287 -0.729287 -0.729287 \n",
"max -0.020471 -0.020471 -0.020471 -0.020471 \n",
"\n",
" LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN LINFOCITOS_MIN \\\n",
"count 334.0 334.000000 334.000000 334.000000 \n",
"mean -1.0 -0.747070 -0.747070 -0.747070 \n",
"std 0.0 0.141870 0.141870 0.141870 \n",
"min -1.0 -0.977178 -0.977178 -0.977178 \n",
"25% -1.0 -0.844398 -0.844398 -0.844398 \n",
"50% -1.0 -0.786307 -0.786307 -0.786307 \n",
"75% -1.0 -0.687241 -0.687241 -0.687241 \n",
"max -1.0 0.080913 0.080913 0.080913 \n",
"\n",
" LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"count 334.000000 334.0 334.000000 \n",
"mean -0.747070 -1.0 -0.833163 \n",
"std 0.141870 0.0 0.110799 \n",
"min -0.977178 -1.0 -0.990796 \n",
"25% -0.844398 -1.0 -0.901861 \n",
"50% -0.786307 -1.0 -0.860844 \n",
"75% -0.687241 -1.0 -0.798419 \n",
"max 0.080913 -1.0 -0.070028 \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN NEUTROPHILES_MAX \\\n",
"count 334.000000 334.000000 334.000000 \n",
"mean -0.833163 -0.833163 -0.833163 \n",
"std 0.110799 0.110799 0.110799 \n",
"min -0.990796 -0.990796 -0.990796 \n",
"25% -0.901861 -0.901861 -0.901861 \n",
"50% -0.860844 -0.860844 -0.860844 \n",
"75% -0.798419 -0.798419 -0.798419 \n",
"max -0.070028 -0.070028 -0.070028 \n",
"\n",
" NEUTROPHILES_DIFF P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN \\\n",
"count 334.0 334.000000 334.000000 \n",
"mean -1.0 -0.175150 -0.175150 \n",
"std 0.0 0.070195 0.070195 \n",
"min -1.0 -1.000000 -1.000000 \n",
"25% -1.0 -0.170732 -0.170732 \n",
"50% -1.0 -0.170732 -0.170732 \n",
"75% -1.0 -0.170732 -0.170732 \n",
"max -1.0 0.310976 0.310976 \n",
"\n",
" P02_ARTERIAL_MIN P02_ARTERIAL_MAX P02_ARTERIAL_DIFF \\\n",
"count 334.000000 334.000000 334.0 \n",
"mean -0.175150 -0.175150 -1.0 \n",
"std 0.070195 0.070195 0.0 \n",
"min -1.000000 -1.000000 -1.0 \n",
"25% -0.170732 -0.170732 -1.0 \n",
"50% -0.170732 -0.170732 -1.0 \n",
"75% -0.170732 -0.170732 -1.0 \n",
"max 0.310976 0.310976 -1.0 \n",
"\n",
" P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN P02_VENOUS_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.696152 -0.696152 -0.696152 -0.696152 \n",
"std 0.128496 0.128496 0.128496 0.128496 \n",
"min -0.988166 -0.988166 -0.988166 -0.988166 \n",
"25% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"50% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"75% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"max 0.372781 0.372781 0.372781 0.372781 \n",
"\n",
" P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"count 334.0 334.000000 334.000000 \n",
"mean -1.0 -0.780171 -0.780171 \n",
"std 0.0 0.010824 0.010824 \n",
"min -1.0 -0.958621 -0.958621 \n",
"25% -1.0 -0.779310 -0.779310 \n",
"50% -1.0 -0.779310 -0.779310 \n",
"75% -1.0 -0.779310 -0.779310 \n",
"max -1.0 -0.737931 -0.737931 \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"count 334.000000 334.000000 334.0 \n",
"mean -0.780171 -0.780171 -1.0 \n",
"std 0.010824 0.010824 0.0 \n",
"min -0.958621 -0.958621 -1.0 \n",
"25% -0.779310 -0.779310 -1.0 \n",
"50% -0.779310 -0.779310 -1.0 \n",
"75% -0.779310 -0.779310 -1.0 \n",
"max -0.737931 -0.737931 -1.0 \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.765781 -0.765781 -0.765781 -0.765781 \n",
"std 0.049559 0.049559 0.049559 0.049559 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"50% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"75% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"max -0.546012 -0.546012 -0.546012 -0.546012 \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX \\\n",
"count 334.0 334.000000 334.000000 334.000000 334.000000 \n",
"mean -1.0 -0.818596 -0.818596 -0.818596 -0.818596 \n",
"std 0.0 0.222292 0.222292 0.222292 0.222292 \n",
"min -1.0 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -1.0 -0.974953 -0.974953 -0.974953 -0.974953 \n",
"50% -1.0 -0.891682 -0.891682 -0.891682 -0.891682 \n",
"75% -1.0 -0.753403 -0.753403 -0.753403 -0.753403 \n",
"max -1.0 0.535350 0.535350 0.535350 0.535350 \n",
"\n",
" PCR_DIFF PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN \\\n",
"count 334.0 334.000000 334.000000 334.000000 \n",
"mean -1.0 0.232875 0.232875 0.232875 \n",
"std 0.0 0.044128 0.044128 0.044128 \n",
"min -1.0 -0.489362 -0.489362 -0.489362 \n",
"25% -1.0 0.234043 0.234043 0.234043 \n",
"50% -1.0 0.234043 0.234043 0.234043 \n",
"75% -1.0 0.234043 0.234043 0.234043 \n",
"max -1.0 0.404255 0.404255 0.404255 \n",
"\n",
" PH_ARTERIAL_MAX PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN \\\n",
"count 334.000000 334.0 334.000000 334.000000 \n",
"mean 0.232875 -1.0 0.379952 0.379952 \n",
"std 0.044128 0.0 0.090438 0.090438 \n",
"min -0.489362 -1.0 -0.090909 -0.090909 \n",
"25% 0.234043 -1.0 0.363636 0.363636 \n",
"50% 0.234043 -1.0 0.363636 0.363636 \n",
"75% 0.234043 -1.0 0.363636 0.363636 \n",
"max 0.404255 -1.0 1.000000 1.000000 \n",
"\n",
" PH_VENOUS_MIN PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN \\\n",
"count 334.000000 334.000000 334.0 334.000000 \n",
"mean 0.379952 0.379952 -1.0 -0.501035 \n",
"std 0.090438 0.090438 0.0 0.188765 \n",
"min -0.090909 -0.090909 -1.0 -0.991989 \n",
"25% 0.363636 0.363636 -1.0 -0.615487 \n",
"50% 0.363636 0.363636 -1.0 -0.516689 \n",
"75% 0.363636 0.363636 -1.0 -0.405207 \n",
"max 1.000000 1.000000 -1.0 0.329773 \n",
"\n",
" PLATELETS_MEAN PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.0 \n",
"mean -0.501035 -0.501035 -0.501035 -1.0 \n",
"std 0.188765 0.188765 0.188765 0.0 \n",
"min -0.991989 -0.991989 -0.991989 -1.0 \n",
"25% -0.615487 -0.615487 -0.615487 -1.0 \n",
"50% -0.516689 -0.516689 -0.516689 -1.0 \n",
"75% -0.405207 -0.405207 -0.405207 -1.0 \n",
"max 0.329773 0.329773 0.329773 -1.0 \n",
"\n",
" POTASSIUM_MEDIAN POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX \\\n",
"count 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.558346 -0.558346 -0.558346 -0.558346 \n",
"std 0.157769 0.157769 0.157769 0.157769 \n",
"min -0.962963 -0.962963 -0.962963 -0.962963 \n",
"25% -0.666667 -0.666667 -0.666667 -0.666667 \n",
"50% -0.555556 -0.555556 -0.555556 -0.555556 \n",
"75% -0.481481 -0.481481 -0.481481 -0.481481 \n",
"max 0.222222 0.222222 0.222222 0.222222 \n",
"\n",
" POTASSIUM_DIFF SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN \\\n",
"count 334.0 334.000000 334.000000 \n",
"mean -1.0 0.928083 0.928083 \n",
"std 0.0 0.135022 0.135022 \n",
"min -1.0 -1.000000 -1.000000 \n",
"25% -1.0 0.939394 0.939394 \n",
"50% -1.0 0.939394 0.939394 \n",
"75% -1.0 0.939394 0.939394 \n",
"max -1.0 0.969697 0.969697 \n",
"\n",
" SAT02_ARTERIAL_MIN SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF \\\n",
"count 334.000000 334.000000 334.0 \n",
"mean 0.928083 0.928083 -1.0 \n",
"std 0.135022 0.135022 0.0 \n",
"min -1.000000 -1.000000 -1.0 \n",
"25% 0.939394 0.939394 -1.0 \n",
"50% 0.939394 0.939394 -1.0 \n",
"75% 0.939394 0.939394 -1.0 \n",
"max 0.969697 0.969697 -1.0 \n",
"\n",
" SAT02_VENOUS_MEDIAN SAT02_VENOUS_MEAN SAT02_VENOUS_MIN \\\n",
"count 334.000000 334.000000 334.000000 \n",
"mean 0.299087 0.299087 0.299087 \n",
"std 0.300392 0.300392 0.300392 \n",
"min -0.925926 -0.925926 -0.925926 \n",
"25% 0.345679 0.345679 0.345679 \n",
"50% 0.345679 0.345679 0.345679 \n",
"75% 0.345679 0.345679 0.345679 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" SAT02_VENOUS_MAX SAT02_VENOUS_DIFF SODIUM_MEDIAN SODIUM_MEAN \\\n",
"count 334.000000 334.0 334.000000 334.000000 \n",
"mean 0.299087 -1.0 -0.090048 -0.090048 \n",
"std 0.300392 0.0 0.202813 0.202813 \n",
"min -0.925926 -1.0 -0.771429 -0.771429 \n",
"25% 0.345679 -1.0 -0.200000 -0.200000 \n",
"50% 0.345679 -1.0 -0.057143 -0.057143 \n",
"75% 0.345679 -1.0 0.047619 0.047619 \n",
"max 1.000000 -1.0 1.000000 1.000000 \n",
"\n",
" SODIUM_MIN SODIUM_MAX SODIUM_DIFF TGO_MEDIAN TGO_MEAN \\\n",
"count 334.000000 334.000000 334.0 334.000000 334.000000 \n",
"mean -0.090048 -0.090048 -1.0 -0.993567 -0.993567 \n",
"std 0.202813 0.202813 0.0 0.017891 0.017891 \n",
"min -0.771429 -0.771429 -1.0 -0.999627 -0.999627 \n",
"25% -0.200000 -0.200000 -1.0 -0.996827 -0.996827 \n",
"50% -0.057143 -0.057143 -1.0 -0.995428 -0.995428 \n",
"75% 0.047619 0.047619 -1.0 -0.994588 -0.994588 \n",
"max 1.000000 1.000000 -1.0 -0.693944 -0.693944 \n",
"\n",
" TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN TGP_MIN \\\n",
"count 334.000000 334.000000 334.0 334.000000 334.000000 334.000000 \n",
"mean -0.993567 -0.993567 -1.0 -0.986554 -0.986554 -0.986554 \n",
"std 0.017891 0.017891 0.0 0.015050 0.015050 0.015050 \n",
"min -0.999627 -0.999627 -1.0 -0.999619 -0.999619 -0.999619 \n",
"25% -0.996827 -0.996827 -1.0 -0.993521 -0.993521 -0.993521 \n",
"50% -0.995428 -0.995428 -1.0 -0.988662 -0.988662 -0.988662 \n",
"75% -0.994588 -0.994588 -1.0 -0.986662 -0.986662 -0.986662 \n",
"max -0.693944 -0.693944 -1.0 -0.794970 -0.794970 -0.794970 \n",
"\n",
" TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"count 334.000000 334.0 334.000000 334.000000 334.000000 334.000000 \n",
"mean -0.986554 -1.0 -0.836616 -0.836616 -0.836616 -0.836616 \n",
"std 0.015050 0.0 0.043978 0.043978 0.043978 0.043978 \n",
"min -0.999619 -1.0 -0.961853 -0.961853 -0.961853 -0.961853 \n",
"25% -0.993521 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"50% -0.988662 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"75% -0.986662 -1.0 -0.837096 -0.837096 -0.837096 -0.837096 \n",
"max -0.794970 -1.0 -0.629428 -0.629428 -0.629428 -0.629428 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"count 334.0 334.000000 334.000000 334.000000 334.000000 334.0 \n",
"mean -1.0 -0.852589 -0.852589 -0.852589 -0.852589 -1.0 \n",
"std 0.0 0.074589 0.074589 0.074589 0.074589 0.0 \n",
"min -1.0 -0.978313 -0.978313 -0.978313 -0.978313 -1.0 \n",
"25% -1.0 -0.898795 -0.898795 -0.898795 -0.898795 -1.0 \n",
"50% -1.0 -0.869880 -0.869880 -0.869880 -0.869880 -1.0 \n",
"75% -1.0 -0.826506 -0.826506 -0.826506 -0.826506 -1.0 \n",
"max -1.0 -0.455422 -0.455422 -0.455422 -0.455422 -1.0 \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"count 334.000000 334.000000 334.000000 334.000000 334.0 \n",
"mean -0.953361 -0.953361 -0.953361 -0.953361 -1.0 \n",
"std 0.131882 0.131882 0.131882 0.131882 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.984553 -0.984553 -0.984553 -0.984553 -1.0 \n",
"50% -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"75% -0.964345 -0.964345 -0.964345 -0.964345 -1.0 \n",
"max 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN \\\n",
"count 315.000000 315.000000 \n",
"mean -0.042859 -0.339638 \n",
"std 0.236802 0.264567 \n",
"min -0.654321 -0.880959 \n",
"25% -0.166713 -0.518956 \n",
"50% -0.045578 -0.384615 \n",
"75% 0.086420 -0.215385 \n",
"max 1.000000 1.000000 \n",
"\n",
" HEART_RATE_MEAN RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN \\\n",
"count 316.000000 306.000000 313.000000 \n",
"mean -0.246726 -0.496321 0.104692 \n",
"std 0.257633 0.176096 0.237660 \n",
"min -0.830189 -0.932203 -0.500000 \n",
"25% -0.415094 -0.576824 -0.077068 \n",
"50% -0.274118 -0.525424 0.072368 \n",
"75% -0.132075 -0.457627 0.250000 \n",
"max 0.905660 0.830508 0.964286 \n",
"\n",
" OXYGEN_SATURATION_MEAN BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"count 316.000000 315.000000 \n",
"mean 0.740155 -0.043791 \n",
"std 0.156718 0.239481 \n",
"min -1.000000 -0.679012 \n",
"25% 0.684211 -0.160494 \n",
"50% 0.752539 -0.037037 \n",
"75% 0.831236 0.086420 \n",
"max 1.000000 1.000000 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN \\\n",
"count 315.000000 316.000000 \n",
"mean -0.341907 -0.245776 \n",
"std 0.265640 0.266321 \n",
"min -0.884615 -0.830189 \n",
"25% -0.523077 -0.417453 \n",
"50% -0.384615 -0.272013 \n",
"75% -0.225641 -0.132075 \n",
"max 1.000000 0.962264 \n",
"\n",
" RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN OXYGEN_SATURATION_MEDIAN \\\n",
"count 306.000000 313.000000 316.000000 \n",
"mean -0.488741 0.101438 0.743768 \n",
"std 0.180155 0.240952 0.159115 \n",
"min -0.931034 -0.500000 -1.000000 \n",
"25% -0.568966 -0.080357 0.684211 \n",
"50% -0.517241 0.071429 0.754386 \n",
"75% -0.448276 0.250000 0.842105 \n",
"max 0.862069 0.964286 1.000000 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MIN BLOODPRESSURE_SISTOLIC_MIN \\\n",
"count 315.000000 315.000000 \n",
"mean 0.067534 -0.137126 \n",
"std 0.207192 0.225559 \n",
"min -0.515464 -0.587500 \n",
"25% -0.072165 -0.291667 \n",
"50% 0.030928 -0.166667 \n",
"75% 0.195876 -0.031250 \n",
"max 1.000000 1.000000 \n",
"\n",
" HEART_RATE_MIN RESPIRATORY_RATE_MIN TEMPERATURE_MIN \\\n",
"count 316.000000 306.000000 313.000000 \n",
"mean -0.188963 -0.443044 0.399847 \n",
"std 0.242153 0.194649 0.160015 \n",
"min -0.803419 -0.857143 0.054945 \n",
"25% -0.342593 -0.547619 0.289377 \n",
"50% -0.222222 -0.500000 0.362637 \n",
"75% -0.089744 -0.404762 0.494505 \n",
"max 0.914530 1.000000 0.978022 \n",
"\n",
" OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"count 316.000000 315.000000 \n",
"mean 0.871122 -0.278547 \n",
"std 0.101621 0.184857 \n",
"min 0.040404 -0.760684 \n",
"25% 0.845118 -0.391738 \n",
"50% 0.887205 -0.282051 \n",
"75% 0.919192 -0.162393 \n",
"max 1.000000 0.384615 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"count 315.000000 316.000000 306.000000 \n",
"mean -0.484893 -0.344516 -0.502129 \n",
"std 0.192835 0.213527 0.171294 \n",
"min -0.891892 -0.865672 -0.939394 \n",
"25% -0.610811 -0.470771 -0.575758 \n",
"50% -0.513514 -0.365672 -0.515152 \n",
"75% -0.362162 -0.238806 -0.454545 \n",
"max 0.405405 0.552239 0.636364 \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"count 313.000000 316.000000 315.000000 \n",
"mean -0.028106 0.781299 -0.888180 \n",
"std 0.209891 0.159679 0.138282 \n",
"min -0.594203 -1.000000 -1.000000 \n",
"25% -0.188406 0.736842 -1.000000 \n",
"50% -0.024155 0.789474 -0.947826 \n",
"75% 0.101449 0.870614 -0.798551 \n",
"max 0.768116 1.000000 -0.217391 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"count 315.000000 316.000000 306.000000 \n",
"mean -0.894264 -0.885927 -0.916619 \n",
"std 0.127159 0.139345 0.104626 \n",
"min -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 \n",
"50% -0.959100 -0.940840 -0.948529 \n",
"75% -0.810838 -0.793893 -0.862745 \n",
"max -0.447853 0.160305 -0.323529 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF \\\n",
"count 313.000000 316.000000 \n",
"mean -0.884826 -0.955068 \n",
"std 0.134078 0.092444 \n",
"min -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 \n",
"50% -0.928571 -0.974747 \n",
"75% -0.793651 -0.944444 \n",
"max -0.238095 -0.090909 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF_REL BLOODPRESSURE_SISTOLIC_DIFF_REL \\\n",
"count 315.000000 315.000000 \n",
"mean -0.909164 -0.886210 \n",
"std 0.114055 0.135249 \n",
"min -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 \n",
"50% -0.959079 -0.950031 \n",
"75% -0.836440 -0.791275 \n",
"max -0.333333 -0.456376 \n",
"\n",
" HEART_RATE_DIFF_REL RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"count 316.000000 306.000000 313.000000 \n",
"mean -0.916102 -0.914525 -0.885639 \n",
"std 0.097268 0.105600 0.132334 \n",
"min -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 \n",
"50% -0.954748 -0.945869 -0.928670 \n",
"75% -0.849462 -0.857826 -0.795906 \n",
"max -0.531749 -0.299283 -0.285468 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"count 316.000000 352.000000 352.000000 \n",
"mean -0.955110 0.107955 0.119318 \n",
"std 0.092310 0.310765 0.324624 \n",
"min -1.000000 0.000000 0.000000 \n",
"25% -1.000000 0.000000 0.000000 \n",
"50% -0.975021 0.000000 0.000000 \n",
"75% -0.944811 0.000000 0.000000 \n",
"max -0.091698 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"count 352.000000 352.000000 352.000000 \n",
"mean 0.110795 0.107955 0.093750 \n",
"std 0.314325 0.310765 0.291896 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"count 352.000000 352.000000 352.000000 \n",
"mean 0.085227 0.096591 0.102273 \n",
"std 0.279617 0.295820 0.303438 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th ICU \n",
"count 352.000000 352.000000 352.000000 \n",
"mean 0.079545 0.096591 0.463068 \n",
"std 0.270973 0.295820 0.499344 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 "
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kfSdCEh82kY7"
},
"source": [
"final_data = final_data.dropna(axis = 0) #Now we must have to drop the rows having nan values as there is no data in any window to fill it."
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Cx2OLK16x_u"
},
"source": [
"##Data Analysis\n",
"Visualising the pre preoessed data and trying to get the intution about different characterstics."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 338
},
"id": "O9m8qwov2tmx",
"outputId": "a00ce6fd-7aa9-4e0e-fff4-445db7fdb81a"
},
"source": [
"final_data.describe()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.0</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" <td>293.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.412969</td>\n",
" <td>0.095563</td>\n",
" <td>0.023891</td>\n",
" <td>0.085324</td>\n",
" <td>0.020478</td>\n",
" <td>0.116041</td>\n",
" <td>0.047782</td>\n",
" <td>0.170648</td>\n",
" <td>0.163823</td>\n",
" <td>0.808874</td>\n",
" <td>0.578828</td>\n",
" <td>0.578828</td>\n",
" <td>0.578828</td>\n",
" <td>0.578828</td>\n",
" <td>-1.0</td>\n",
" <td>-0.998479</td>\n",
" <td>-0.998479</td>\n",
" <td>-0.998479</td>\n",
" <td>-0.998479</td>\n",
" <td>-1.0</td>\n",
" <td>-0.971320</td>\n",
" <td>-0.971320</td>\n",
" <td>-0.971320</td>\n",
" <td>-0.971320</td>\n",
" <td>-1.0</td>\n",
" <td>-0.316796</td>\n",
" <td>-0.316796</td>\n",
" <td>-0.316796</td>\n",
" <td>-0.316796</td>\n",
" <td>-1.0</td>\n",
" <td>-0.321152</td>\n",
" <td>-0.321152</td>\n",
" <td>-0.321152</td>\n",
" <td>-0.321152</td>\n",
" <td>-1.0</td>\n",
" <td>-0.944446</td>\n",
" <td>-0.944446</td>\n",
" <td>-0.944446</td>\n",
" <td>-0.944446</td>\n",
" <td>-1.0</td>\n",
" <td>-0.992285</td>\n",
" <td>-0.992285</td>\n",
" <td>-0.992285</td>\n",
" <td>-0.992285</td>\n",
" <td>-1.0</td>\n",
" <td>0.332045</td>\n",
" <td>0.332045</td>\n",
" <td>0.332045</td>\n",
" <td>0.332045</td>\n",
" <td>-1.0</td>\n",
" <td>-0.892396</td>\n",
" <td>-0.892396</td>\n",
" <td>-0.892396</td>\n",
" <td>-0.892396</td>\n",
" <td>-1.0</td>\n",
" <td>-0.721727</td>\n",
" <td>-0.721727</td>\n",
" <td>-0.721727</td>\n",
" <td>-0.721727</td>\n",
" <td>-1.0</td>\n",
" <td>-0.930257</td>\n",
" <td>-0.930257</td>\n",
" <td>-0.930257</td>\n",
" <td>-0.930257</td>\n",
" <td>-1.0</td>\n",
" <td>-0.859128</td>\n",
" <td>-0.859128</td>\n",
" <td>-0.859128</td>\n",
" <td>-0.859128</td>\n",
" <td>-1.0</td>\n",
" <td>-0.113694</td>\n",
" <td>-0.113694</td>\n",
" <td>-0.113694</td>\n",
" <td>-0.113694</td>\n",
" <td>-1.0</td>\n",
" <td>-0.148398</td>\n",
" <td>-0.148398</td>\n",
" <td>-0.148398</td>\n",
" <td>-0.148398</td>\n",
" <td>-1.0</td>\n",
" <td>-0.943534</td>\n",
" <td>-0.943534</td>\n",
" <td>-0.943534</td>\n",
" <td>-0.943534</td>\n",
" <td>-1.0</td>\n",
" <td>0.582525</td>\n",
" <td>0.582525</td>\n",
" <td>0.582525</td>\n",
" <td>0.582525</td>\n",
" <td>-1.0</td>\n",
" <td>-0.772004</td>\n",
" <td>-0.772004</td>\n",
" <td>-0.772004</td>\n",
" <td>-0.772004</td>\n",
" <td>-1.0</td>\n",
" <td>-0.741621</td>\n",
" <td>-0.741621</td>\n",
" <td>-0.741621</td>\n",
" <td>-0.741621</td>\n",
" <td>-1.0</td>\n",
" <td>-0.835662</td>\n",
" <td>-0.835662</td>\n",
" <td>-0.835662</td>\n",
" <td>-0.835662</td>\n",
" <td>-1.0</td>\n",
" <td>-0.174020</td>\n",
" <td>-0.174020</td>\n",
" <td>-0.174020</td>\n",
" <td>-0.174020</td>\n",
" <td>-1.0</td>\n",
" <td>-0.695438</td>\n",
" <td>-0.695438</td>\n",
" <td>-0.695438</td>\n",
" <td>-0.695438</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779820</td>\n",
" <td>-0.779820</td>\n",
" <td>-0.779820</td>\n",
" <td>-0.779820</td>\n",
" <td>-1.0</td>\n",
" <td>-0.762111</td>\n",
" <td>-0.762111</td>\n",
" <td>-0.762111</td>\n",
" <td>-0.762111</td>\n",
" <td>-1.0</td>\n",
" <td>-0.827216</td>\n",
" <td>-0.827216</td>\n",
" <td>-0.827216</td>\n",
" <td>-0.827216</td>\n",
" <td>-1.0</td>\n",
" <td>0.235906</td>\n",
" <td>0.235906</td>\n",
" <td>0.235906</td>\n",
" <td>0.235906</td>\n",
" <td>-1.0</td>\n",
" <td>0.373444</td>\n",
" <td>0.373444</td>\n",
" <td>0.373444</td>\n",
" <td>0.373444</td>\n",
" <td>-1.0</td>\n",
" <td>-0.495099</td>\n",
" <td>-0.495099</td>\n",
" <td>-0.495099</td>\n",
" <td>-0.495099</td>\n",
" <td>-1.0</td>\n",
" <td>-0.564425</td>\n",
" <td>-0.564425</td>\n",
" <td>-0.564425</td>\n",
" <td>-0.564425</td>\n",
" <td>-1.0</td>\n",
" <td>0.931672</td>\n",
" <td>0.931672</td>\n",
" <td>0.931672</td>\n",
" <td>0.931672</td>\n",
" <td>-1.0</td>\n",
" <td>0.301416</td>\n",
" <td>0.301416</td>\n",
" <td>0.301416</td>\n",
" <td>0.301416</td>\n",
" <td>-1.0</td>\n",
" <td>-0.077133</td>\n",
" <td>-0.077133</td>\n",
" <td>-0.077133</td>\n",
" <td>-0.077133</td>\n",
" <td>-1.0</td>\n",
" <td>-0.993438</td>\n",
" <td>-0.993438</td>\n",
" <td>-0.993438</td>\n",
" <td>-0.993438</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986212</td>\n",
" <td>-0.986212</td>\n",
" <td>-0.986212</td>\n",
" <td>-0.986212</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836803</td>\n",
" <td>-0.836803</td>\n",
" <td>-0.836803</td>\n",
" <td>-0.836803</td>\n",
" <td>-1.0</td>\n",
" <td>-0.854512</td>\n",
" <td>-0.854512</td>\n",
" <td>-0.854512</td>\n",
" <td>-0.854512</td>\n",
" <td>-1.0</td>\n",
" <td>-0.958309</td>\n",
" <td>-0.958309</td>\n",
" <td>-0.958309</td>\n",
" <td>-0.958309</td>\n",
" <td>-1.0</td>\n",
" <td>-0.044813</td>\n",
" <td>-0.353956</td>\n",
" <td>-0.246903</td>\n",
" <td>-0.502897</td>\n",
" <td>0.104200</td>\n",
" <td>0.747809</td>\n",
" <td>-0.045815</td>\n",
" <td>-0.356395</td>\n",
" <td>-0.245879</td>\n",
" <td>-0.495243</td>\n",
" <td>0.101170</td>\n",
" <td>0.751497</td>\n",
" <td>0.061275</td>\n",
" <td>-0.152414</td>\n",
" <td>-0.193791</td>\n",
" <td>-0.451487</td>\n",
" <td>0.396461</td>\n",
" <td>0.872175</td>\n",
" <td>-0.275483</td>\n",
" <td>-0.491120</td>\n",
" <td>-0.339977</td>\n",
" <td>-0.506516</td>\n",
" <td>-0.024089</td>\n",
" <td>0.791974</td>\n",
" <td>-0.879784</td>\n",
" <td>-0.886325</td>\n",
" <td>-0.876972</td>\n",
" <td>-0.913923</td>\n",
" <td>-0.877858</td>\n",
" <td>-0.952023</td>\n",
" <td>-0.902344</td>\n",
" <td>-0.877666</td>\n",
" <td>-0.909516</td>\n",
" <td>-0.911811</td>\n",
" <td>-0.878712</td>\n",
" <td>-0.952054</td>\n",
" <td>0.122867</td>\n",
" <td>0.129693</td>\n",
" <td>0.116041</td>\n",
" <td>0.112628</td>\n",
" <td>0.102389</td>\n",
" <td>0.095563</td>\n",
" <td>0.102389</td>\n",
" <td>0.088737</td>\n",
" <td>0.068259</td>\n",
" <td>0.061433</td>\n",
" <td>0.358362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.493210</td>\n",
" <td>0.294494</td>\n",
" <td>0.152970</td>\n",
" <td>0.279842</td>\n",
" <td>0.141870</td>\n",
" <td>0.320822</td>\n",
" <td>0.213669</td>\n",
" <td>0.376845</td>\n",
" <td>0.370748</td>\n",
" <td>0.393861</td>\n",
" <td>0.122129</td>\n",
" <td>0.122129</td>\n",
" <td>0.122129</td>\n",
" <td>0.122129</td>\n",
" <td>0.0</td>\n",
" <td>0.014321</td>\n",
" <td>0.014321</td>\n",
" <td>0.014321</td>\n",
" <td>0.014321</td>\n",
" <td>0.0</td>\n",
" <td>0.090151</td>\n",
" <td>0.090151</td>\n",
" <td>0.090151</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
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" <tr>\n",
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" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>0.939394</td>\n",
" <td>-1.0</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>0.345679</td>\n",
" <td>-1.0</td>\n",
" <td>0.066667</td>\n",
" <td>0.066667</td>\n",
" <td>0.066667</td>\n",
" <td>0.066667</td>\n",
" <td>-1.0</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-0.994588</td>\n",
" <td>-1.0</td>\n",
" <td>-0.986280</td>\n",
" <td>-0.986280</td>\n",
" <td>-0.986280</td>\n",
" <td>-0.986280</td>\n",
" <td>-1.0</td>\n",
" <td>-0.836512</td>\n",
" <td>-0.836512</td>\n",
" <td>-0.836512</td>\n",
" <td>-0.836512</td>\n",
" <td>-1.0</td>\n",
" <td>-0.831325</td>\n",
" <td>-0.831325</td>\n",
" <td>-0.831325</td>\n",
" <td>-0.831325</td>\n",
" <td>-1.0</td>\n",
" <td>-0.966876</td>\n",
" <td>-0.966876</td>\n",
" <td>-0.966876</td>\n",
" <td>-0.966876</td>\n",
" <td>-1.0</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230605</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.457627</td>\n",
" <td>0.248397</td>\n",
" <td>0.830409</td>\n",
" <td>0.086420</td>\n",
" <td>-0.230769</td>\n",
" <td>-0.132075</td>\n",
" <td>-0.448276</td>\n",
" <td>0.250000</td>\n",
" <td>0.842105</td>\n",
" <td>0.195876</td>\n",
" <td>-0.050000</td>\n",
" <td>-0.094017</td>\n",
" <td>-0.404762</td>\n",
" <td>0.494505</td>\n",
" <td>0.919192</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.365766</td>\n",
" <td>-0.238806</td>\n",
" <td>-0.454545</td>\n",
" <td>0.101449</td>\n",
" <td>0.877193</td>\n",
" <td>-0.785507</td>\n",
" <td>-0.803681</td>\n",
" <td>-0.786260</td>\n",
" <td>-0.852941</td>\n",
" <td>-0.785714</td>\n",
" <td>-0.939394</td>\n",
" <td>-0.831621</td>\n",
" <td>-0.783383</td>\n",
" <td>-0.839287</td>\n",
" <td>-0.851852</td>\n",
" <td>-0.785121</td>\n",
" <td>-0.940644</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-0.786096</td>\n",
" <td>-1.0</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-0.204188</td>\n",
" <td>-1.0</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-0.219512</td>\n",
" <td>-1.0</td>\n",
" <td>-0.073171</td>\n",
" <td>-0.073171</td>\n",
" <td>-0.073171</td>\n",
" <td>-0.073171</td>\n",
" <td>-1.0</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-0.293564</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>0.693878</td>\n",
" <td>-1.0</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>0.493277</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.199255</td>\n",
" <td>-0.199255</td>\n",
" <td>-0.199255</td>\n",
" <td>-0.199255</td>\n",
" <td>-1.0</td>\n",
" <td>0.530398</td>\n",
" <td>0.530398</td>\n",
" <td>0.530398</td>\n",
" <td>0.530398</td>\n",
" <td>-1.0</td>\n",
" <td>0.463415</td>\n",
" <td>0.463415</td>\n",
" <td>0.463415</td>\n",
" <td>0.463415</td>\n",
" <td>-1.0</td>\n",
" <td>-0.606023</td>\n",
" <td>-0.606023</td>\n",
" <td>-0.606023</td>\n",
" <td>-0.606023</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-0.020471</td>\n",
" <td>-1.0</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>0.080913</td>\n",
" <td>-1.0</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-0.070028</td>\n",
" <td>-1.0</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>0.310976</td>\n",
" <td>-1.0</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>0.372781</td>\n",
" <td>-1.0</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-0.779310</td>\n",
" <td>-1.0</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-0.546012</td>\n",
" <td>-1.0</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>0.535350</td>\n",
" <td>-1.0</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>0.404255</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>0.329773</td>\n",
" <td>-1.0</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>0.222222</td>\n",
" <td>-1.0</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>0.969697</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-0.693944</td>\n",
" <td>-1.0</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-0.794970</td>\n",
" <td>-1.0</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-0.629428</td>\n",
" <td>-1.0</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-0.455422</td>\n",
" <td>-1.0</td>\n",
" <td>-0.236287</td>\n",
" <td>-0.236287</td>\n",
" <td>-0.236287</td>\n",
" <td>-0.236287</td>\n",
" <td>-1.0</td>\n",
" <td>0.555556</td>\n",
" <td>0.476923</td>\n",
" <td>0.660377</td>\n",
" <td>0.830508</td>\n",
" <td>0.964286</td>\n",
" <td>1.000000</td>\n",
" <td>0.555556</td>\n",
" <td>0.476923</td>\n",
" <td>0.962264</td>\n",
" <td>0.862069</td>\n",
" <td>0.964286</td>\n",
" <td>1.000000</td>\n",
" <td>0.628866</td>\n",
" <td>0.575000</td>\n",
" <td>0.692308</td>\n",
" <td>1.000000</td>\n",
" <td>0.978022</td>\n",
" <td>1.000000</td>\n",
" <td>0.350427</td>\n",
" <td>0.048649</td>\n",
" <td>0.552239</td>\n",
" <td>0.636364</td>\n",
" <td>0.768116</td>\n",
" <td>1.000000</td>\n",
" <td>-0.217391</td>\n",
" <td>-0.447853</td>\n",
" <td>0.160305</td>\n",
" <td>-0.323529</td>\n",
" <td>-0.238095</td>\n",
" <td>-0.090909</td>\n",
" <td>-0.333333</td>\n",
" <td>-0.456376</td>\n",
" <td>-0.531749</td>\n",
" <td>-0.299283</td>\n",
" <td>-0.285468</td>\n",
" <td>-0.091698</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 1 DISEASE GROUPING 2 \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.412969 0.095563 0.023891 \n",
"std 0.493210 0.294494 0.152970 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 1.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" DISEASE GROUPING 3 DISEASE GROUPING 4 DISEASE GROUPING 5 \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.085324 0.020478 0.116041 \n",
"std 0.279842 0.141870 0.320822 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean 0.047782 0.170648 0.163823 0.808874 \n",
"std 0.213669 0.376845 0.370748 0.393861 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 1.000000 \n",
"50% 0.000000 0.000000 0.000000 1.000000 \n",
"75% 0.000000 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" ALBUMIN_MEDIAN ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX ALBUMIN_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.000000 293.0 \n",
"mean 0.578828 0.578828 0.578828 0.578828 -1.0 \n",
"std 0.122129 0.122129 0.122129 0.122129 0.0 \n",
"min -0.578947 -0.578947 -0.578947 -0.578947 -1.0 \n",
"25% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"50% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"75% 0.605263 0.605263 0.605263 0.605263 -1.0 \n",
"max 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN BE_ARTERIAL_MIN BE_ARTERIAL_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.998479 -0.998479 -0.998479 -0.998479 \n",
"std 0.014321 0.014321 0.014321 0.014321 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"50% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"75% -1.000000 -1.000000 -1.000000 -1.000000 \n",
"max -0.786096 -0.786096 -0.786096 -0.786096 \n",
"\n",
" BE_ARTERIAL_DIFF BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"count 293.0 293.000000 293.000000 293.000000 \n",
"mean -1.0 -0.971320 -0.971320 -0.971320 \n",
"std 0.0 0.090151 0.090151 0.090151 \n",
"min -1.0 -1.000000 -1.000000 -1.000000 \n",
"25% -1.0 -1.000000 -1.000000 -1.000000 \n",
"50% -1.0 -1.000000 -1.000000 -1.000000 \n",
"75% -1.0 -1.000000 -1.000000 -1.000000 \n",
"max -1.0 -0.204188 -0.204188 -0.204188 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN BIC_ARTERIAL_MEAN \\\n",
"count 293.000000 293.0 293.000000 293.000000 \n",
"mean -0.971320 -1.0 -0.316796 -0.316796 \n",
"std 0.090151 0.0 0.007051 0.007051 \n",
"min -1.000000 -1.0 -0.365854 -0.365854 \n",
"25% -1.000000 -1.0 -0.317073 -0.317073 \n",
"50% -1.000000 -1.0 -0.317073 -0.317073 \n",
"75% -1.000000 -1.0 -0.317073 -0.317073 \n",
"max -0.204188 -1.0 -0.219512 -0.219512 \n",
"\n",
" BIC_ARTERIAL_MIN BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF \\\n",
"count 293.000000 293.000000 293.0 \n",
"mean -0.316796 -0.316796 -1.0 \n",
"std 0.007051 0.007051 0.0 \n",
"min -0.365854 -0.365854 -1.0 \n",
"25% -0.317073 -0.317073 -1.0 \n",
"50% -0.317073 -0.317073 -1.0 \n",
"75% -0.317073 -0.317073 -1.0 \n",
"max -0.219512 -0.219512 -1.0 \n",
"\n",
" BIC_VENOUS_MEDIAN BIC_VENOUS_MEAN BIC_VENOUS_MIN BIC_VENOUS_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.321152 -0.321152 -0.321152 -0.321152 \n",
"std 0.077980 0.077980 0.077980 0.077980 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"50% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"75% -0.317073 -0.317073 -0.317073 -0.317073 \n",
"max -0.073171 -0.073171 -0.073171 -0.073171 \n",
"\n",
" BIC_VENOUS_DIFF BILLIRUBIN_MEDIAN BILLIRUBIN_MEAN BILLIRUBIN_MIN \\\n",
"count 293.0 293.000000 293.000000 293.000000 \n",
"mean -1.0 -0.944446 -0.944446 -0.944446 \n",
"std 0.0 0.045846 0.045846 0.045846 \n",
"min -1.0 -0.994767 -0.994767 -0.994767 \n",
"25% -1.0 -0.962323 -0.962323 -0.962323 \n",
"50% -1.0 -0.938950 -0.938950 -0.938950 \n",
"75% -1.0 -0.938950 -0.938950 -0.938950 \n",
"max -1.0 -0.293564 -0.293564 -0.293564 \n",
"\n",
" BILLIRUBIN_MAX BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN BLAST_MIN \\\n",
"count 293.000000 293.0 293.000000 293.000000 293.000000 \n",
"mean -0.944446 -1.0 -0.992285 -0.992285 -0.992285 \n",
"std 0.045846 0.0 0.117776 0.117776 0.117776 \n",
"min -0.994767 -1.0 -1.000000 -1.000000 -1.000000 \n",
"25% -0.962323 -1.0 -1.000000 -1.000000 -1.000000 \n",
"50% -0.938950 -1.0 -1.000000 -1.000000 -1.000000 \n",
"75% -0.938950 -1.0 -1.000000 -1.000000 -1.000000 \n",
"max -0.293564 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" BLAST_MAX BLAST_DIFF CALCIUM_MEDIAN CALCIUM_MEAN CALCIUM_MIN \\\n",
"count 293.000000 293.0 293.000000 293.000000 293.000000 \n",
"mean -0.992285 -1.0 0.332045 0.332045 0.332045 \n",
"std 0.117776 0.0 0.078696 0.078696 0.078696 \n",
"min -1.000000 -1.0 0.020408 0.020408 0.020408 \n",
"25% -1.000000 -1.0 0.336735 0.336735 0.336735 \n",
"50% -1.000000 -1.0 0.357143 0.357143 0.357143 \n",
"75% -1.000000 -1.0 0.357143 0.357143 0.357143 \n",
"max 1.000000 -1.0 0.693878 0.693878 0.693878 \n",
"\n",
" CALCIUM_MAX CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN \\\n",
"count 293.000000 293.0 293.000000 293.000000 \n",
"mean 0.332045 -1.0 -0.892396 -0.892396 \n",
"std 0.078696 0.0 0.102629 0.102629 \n",
"min 0.020408 -1.0 -0.970276 -0.970276 \n",
"25% 0.336735 -1.0 -0.929229 -0.929229 \n",
"50% 0.357143 -1.0 -0.908402 -0.908402 \n",
"75% 0.357143 -1.0 -0.881104 -0.881104 \n",
"max 0.693878 -1.0 0.493277 0.493277 \n",
"\n",
" CREATININ_MIN CREATININ_MAX CREATININ_DIFF FFA_MEDIAN FFA_MEAN \\\n",
"count 293.000000 293.000000 293.0 293.000000 293.000000 \n",
"mean -0.892396 -0.892396 -1.0 -0.721727 -0.721727 \n",
"std 0.102629 0.102629 0.0 0.184035 0.184035 \n",
"min -0.970276 -0.970276 -1.0 -0.918977 -0.918977 \n",
"25% -0.929229 -0.929229 -1.0 -0.744136 -0.744136 \n",
"50% -0.908402 -0.908402 -1.0 -0.742004 -0.742004 \n",
"75% -0.881104 -0.881104 -1.0 -0.742004 -0.742004 \n",
"max 0.493277 0.493277 -1.0 1.000000 1.000000 \n",
"\n",
" FFA_MIN FFA_MAX FFA_DIFF GGT_MEDIAN GGT_MEAN GGT_MIN \\\n",
"count 293.000000 293.000000 293.0 293.000000 293.000000 293.000000 \n",
"mean -0.721727 -0.721727 -1.0 -0.930257 -0.930257 -0.930257 \n",
"std 0.184035 0.184035 0.0 0.145172 0.145172 0.145172 \n",
"min -0.918977 -0.918977 -1.0 -0.997664 -0.997664 -0.997664 \n",
"25% -0.744136 -0.744136 -1.0 -0.958528 -0.958528 -0.958528 \n",
"50% -0.742004 -0.742004 -1.0 -0.958528 -0.958528 -0.958528 \n",
"75% -0.742004 -0.742004 -1.0 -0.958528 -0.958528 -0.958528 \n",
"max 1.000000 1.000000 -1.0 1.000000 1.000000 1.000000 \n",
"\n",
" GGT_MAX GGT_DIFF GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN \\\n",
"count 293.000000 293.0 293.000000 293.000000 293.000000 \n",
"mean -0.930257 -1.0 -0.859128 -0.859128 -0.859128 \n",
"std 0.145172 0.0 0.089202 0.089202 0.089202 \n",
"min -0.997664 -1.0 -0.929236 -0.929236 -0.929236 \n",
"25% -0.958528 -1.0 -0.891993 -0.891993 -0.891993 \n",
"50% -0.958528 -1.0 -0.891993 -0.891993 -0.891993 \n",
"75% -0.958528 -1.0 -0.865922 -0.865922 -0.865922 \n",
"max 1.000000 -1.0 -0.199255 -0.199255 -0.199255 \n",
"\n",
" GLUCOSE_MAX GLUCOSE_DIFF HEMATOCRITE_MEDIAN HEMATOCRITE_MEAN \\\n",
"count 293.000000 293.0 293.000000 293.000000 \n",
"mean -0.859128 -1.0 -0.113694 -0.113694 \n",
"std 0.089202 0.0 0.206982 0.206982 \n",
"min -0.929236 -1.0 -0.903564 -0.903564 \n",
"25% -0.891993 -1.0 -0.220126 -0.220126 \n",
"50% -0.891993 -1.0 -0.104822 -0.104822 \n",
"75% -0.865922 -1.0 0.023061 0.023061 \n",
"max -0.199255 -1.0 0.530398 0.530398 \n",
"\n",
" HEMATOCRITE_MIN HEMATOCRITE_MAX HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN \\\n",
"count 293.000000 293.000000 293.0 293.000000 \n",
"mean -0.113694 -0.113694 -1.0 -0.148398 \n",
"std 0.206982 0.206982 0.0 0.219247 \n",
"min -0.903564 -0.903564 -1.0 -1.000000 \n",
"25% -0.220126 -0.220126 -1.0 -0.268293 \n",
"50% -0.104822 -0.104822 -1.0 -0.146341 \n",
"75% 0.023061 0.023061 -1.0 0.012195 \n",
"max 0.530398 0.530398 -1.0 0.463415 \n",
"\n",
" HEMOGLOBIN_MEAN HEMOGLOBIN_MIN HEMOGLOBIN_MAX HEMOGLOBIN_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.0 \n",
"mean -0.148398 -0.148398 -0.148398 -1.0 \n",
"std 0.219247 0.219247 0.219247 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.268293 -0.268293 -0.268293 -1.0 \n",
"50% -0.146341 -0.146341 -0.146341 -1.0 \n",
"75% 0.012195 0.012195 0.012195 -1.0 \n",
"max 0.463415 0.463415 0.463415 -1.0 \n",
"\n",
" INR_MEDIAN INR_MEAN INR_MIN INR_MAX INR_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.000000 293.0 \n",
"mean -0.943534 -0.943534 -0.943534 -0.943534 -1.0 \n",
"std 0.045584 0.045584 0.045584 0.045584 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"50% -0.959849 -0.959849 -0.959849 -0.959849 -1.0 \n",
"75% -0.946048 -0.946048 -0.946048 -0.946048 -1.0 \n",
"max -0.606023 -0.606023 -0.606023 -0.606023 -1.0 \n",
"\n",
" LACTATE_MEDIAN LACTATE_MEAN LACTATE_MIN LACTATE_MAX LACTATE_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.000000 293.0 \n",
"mean 0.582525 0.582525 0.582525 0.582525 -1.0 \n",
"std 0.727411 0.727411 0.727411 0.727411 0.0 \n",
"min -0.975884 -0.975884 -0.975884 -0.975884 -1.0 \n",
"25% 0.068451 0.068451 0.068451 0.068451 -1.0 \n",
"50% 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"75% 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"max 1.000000 1.000000 1.000000 1.000000 -1.0 \n",
"\n",
" LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN LEUKOCYTES_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.772004 -0.772004 -0.772004 -0.772004 \n",
"std 0.116851 0.116851 0.116851 0.116851 \n",
"min -0.966010 -0.966010 -0.966010 -0.966010 \n",
"25% -0.842410 -0.842410 -0.842410 -0.842410 \n",
"50% -0.799923 -0.799923 -0.799923 -0.799923 \n",
"75% -0.736964 -0.736964 -0.736964 -0.736964 \n",
"max -0.020471 -0.020471 -0.020471 -0.020471 \n",
"\n",
" LEUKOCYTES_DIFF LINFOCITOS_MEDIAN LINFOCITOS_MEAN LINFOCITOS_MIN \\\n",
"count 293.0 293.000000 293.000000 293.000000 \n",
"mean -1.0 -0.741621 -0.741621 -0.741621 \n",
"std 0.0 0.145799 0.145799 0.145799 \n",
"min -1.0 -0.977178 -0.977178 -0.977178 \n",
"25% -1.0 -0.836100 -0.836100 -0.836100 \n",
"50% -1.0 -0.776971 -0.776971 -0.776971 \n",
"75% -1.0 -0.684647 -0.684647 -0.684647 \n",
"max -1.0 0.080913 0.080913 0.080913 \n",
"\n",
" LINFOCITOS_MAX LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"count 293.000000 293.0 293.000000 \n",
"mean -0.741621 -1.0 -0.835662 \n",
"std 0.145799 0.0 0.112248 \n",
"min -0.977178 -1.0 -0.990796 \n",
"25% -0.836100 -1.0 -0.902361 \n",
"50% -0.776971 -1.0 -0.862145 \n",
"75% -0.684647 -1.0 -0.806723 \n",
"max 0.080913 -1.0 -0.070028 \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN NEUTROPHILES_MAX \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.835662 -0.835662 -0.835662 \n",
"std 0.112248 0.112248 0.112248 \n",
"min -0.990796 -0.990796 -0.990796 \n",
"25% -0.902361 -0.902361 -0.902361 \n",
"50% -0.862145 -0.862145 -0.862145 \n",
"75% -0.806723 -0.806723 -0.806723 \n",
"max -0.070028 -0.070028 -0.070028 \n",
"\n",
" NEUTROPHILES_DIFF P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN \\\n",
"count 293.0 293.000000 293.000000 \n",
"mean -1.0 -0.174020 -0.174020 \n",
"std 0.0 0.061538 0.061538 \n",
"min -1.0 -1.000000 -1.000000 \n",
"25% -1.0 -0.170732 -0.170732 \n",
"50% -1.0 -0.170732 -0.170732 \n",
"75% -1.0 -0.170732 -0.170732 \n",
"max -1.0 0.310976 0.310976 \n",
"\n",
" P02_ARTERIAL_MIN P02_ARTERIAL_MAX P02_ARTERIAL_DIFF \\\n",
"count 293.000000 293.000000 293.0 \n",
"mean -0.174020 -0.174020 -1.0 \n",
"std 0.061538 0.061538 0.0 \n",
"min -1.000000 -1.000000 -1.0 \n",
"25% -0.170732 -0.170732 -1.0 \n",
"50% -0.170732 -0.170732 -1.0 \n",
"75% -0.170732 -0.170732 -1.0 \n",
"max 0.310976 0.310976 -1.0 \n",
"\n",
" P02_VENOUS_MEDIAN P02_VENOUS_MEAN P02_VENOUS_MIN P02_VENOUS_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.695438 -0.695438 -0.695438 -0.695438 \n",
"std 0.129847 0.129847 0.129847 0.129847 \n",
"min -0.952663 -0.952663 -0.952663 -0.952663 \n",
"25% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"50% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"75% -0.704142 -0.704142 -0.704142 -0.704142 \n",
"max 0.372781 0.372781 0.372781 0.372781 \n",
"\n",
" P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN PC02_ARTERIAL_MEAN \\\n",
"count 293.0 293.000000 293.000000 \n",
"mean -1.0 -0.779820 -0.779820 \n",
"std 0.0 0.004268 0.004268 \n",
"min -1.0 -0.825287 -0.825287 \n",
"25% -1.0 -0.779310 -0.779310 \n",
"50% -1.0 -0.779310 -0.779310 \n",
"75% -1.0 -0.779310 -0.779310 \n",
"max -1.0 -0.779310 -0.779310 \n",
"\n",
" PC02_ARTERIAL_MIN PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"count 293.000000 293.000000 293.0 \n",
"mean -0.779820 -0.779820 -1.0 \n",
"std 0.004268 0.004268 0.0 \n",
"min -0.825287 -0.825287 -1.0 \n",
"25% -0.779310 -0.779310 -1.0 \n",
"50% -0.779310 -0.779310 -1.0 \n",
"75% -0.779310 -0.779310 -1.0 \n",
"max -0.779310 -0.779310 -1.0 \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN PC02_VENOUS_MIN PC02_VENOUS_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.762111 -0.762111 -0.762111 -0.762111 \n",
"std 0.044664 0.044664 0.044664 0.044664 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"50% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"75% -0.754601 -0.754601 -0.754601 -0.754601 \n",
"max -0.546012 -0.546012 -0.546012 -0.546012 \n",
"\n",
" PC02_VENOUS_DIFF PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX \\\n",
"count 293.0 293.000000 293.000000 293.000000 293.000000 \n",
"mean -1.0 -0.827216 -0.827216 -0.827216 -0.827216 \n",
"std 0.0 0.212602 0.212602 0.212602 0.212602 \n",
"min -1.0 -1.000000 -1.000000 -1.000000 -1.000000 \n",
"25% -1.0 -0.975614 -0.975614 -0.975614 -0.975614 \n",
"50% -1.0 -0.899433 -0.899433 -0.899433 -0.899433 \n",
"75% -1.0 -0.774291 -0.774291 -0.774291 -0.774291 \n",
"max -1.0 0.535350 0.535350 0.535350 0.535350 \n",
"\n",
" PCR_DIFF PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN PH_ARTERIAL_MIN \\\n",
"count 293.0 293.000000 293.000000 293.000000 \n",
"mean -1.0 0.235906 0.235906 0.235906 \n",
"std 0.0 0.016481 0.016481 0.016481 \n",
"min -1.0 0.191489 0.191489 0.191489 \n",
"25% -1.0 0.234043 0.234043 0.234043 \n",
"50% -1.0 0.234043 0.234043 0.234043 \n",
"75% -1.0 0.234043 0.234043 0.234043 \n",
"max -1.0 0.404255 0.404255 0.404255 \n",
"\n",
" PH_ARTERIAL_MAX PH_ARTERIAL_DIFF PH_VENOUS_MEDIAN PH_VENOUS_MEAN \\\n",
"count 293.000000 293.0 293.000000 293.000000 \n",
"mean 0.235906 -1.0 0.373444 0.373444 \n",
"std 0.016481 0.0 0.080393 0.080393 \n",
"min 0.191489 -1.0 -0.090909 -0.090909 \n",
"25% 0.234043 -1.0 0.363636 0.363636 \n",
"50% 0.234043 -1.0 0.363636 0.363636 \n",
"75% 0.234043 -1.0 0.363636 0.363636 \n",
"max 0.404255 -1.0 1.000000 1.000000 \n",
"\n",
" PH_VENOUS_MIN PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN \\\n",
"count 293.000000 293.000000 293.0 293.000000 \n",
"mean 0.373444 0.373444 -1.0 -0.495099 \n",
"std 0.080393 0.080393 0.0 0.190213 \n",
"min -0.090909 -0.090909 -1.0 -0.991989 \n",
"25% 0.363636 0.363636 -1.0 -0.615487 \n",
"50% 0.363636 0.363636 -1.0 -0.514019 \n",
"75% 0.363636 0.363636 -1.0 -0.403204 \n",
"max 1.000000 1.000000 -1.0 0.329773 \n",
"\n",
" PLATELETS_MEAN PLATELETS_MIN PLATELETS_MAX PLATELETS_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.0 \n",
"mean -0.495099 -0.495099 -0.495099 -1.0 \n",
"std 0.190213 0.190213 0.190213 0.0 \n",
"min -0.991989 -0.991989 -0.991989 -1.0 \n",
"25% -0.615487 -0.615487 -0.615487 -1.0 \n",
"50% -0.514019 -0.514019 -0.514019 -1.0 \n",
"75% -0.403204 -0.403204 -0.403204 -1.0 \n",
"max 0.329773 0.329773 0.329773 -1.0 \n",
"\n",
" POTASSIUM_MEDIAN POTASSIUM_MEAN POTASSIUM_MIN POTASSIUM_MAX \\\n",
"count 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.564425 -0.564425 -0.564425 -0.564425 \n",
"std 0.149514 0.149514 0.149514 0.149514 \n",
"min -0.962963 -0.962963 -0.962963 -0.962963 \n",
"25% -0.666667 -0.666667 -0.666667 -0.666667 \n",
"50% -0.555556 -0.555556 -0.555556 -0.555556 \n",
"75% -0.481481 -0.481481 -0.481481 -0.481481 \n",
"max 0.222222 0.222222 0.222222 0.222222 \n",
"\n",
" POTASSIUM_DIFF SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN \\\n",
"count 293.0 293.000000 293.000000 \n",
"mean -1.0 0.931672 0.931672 \n",
"std 0.0 0.114085 0.114085 \n",
"min -1.0 -1.000000 -1.000000 \n",
"25% -1.0 0.939394 0.939394 \n",
"50% -1.0 0.939394 0.939394 \n",
"75% -1.0 0.939394 0.939394 \n",
"max -1.0 0.969697 0.969697 \n",
"\n",
" SAT02_ARTERIAL_MIN SAT02_ARTERIAL_MAX SAT02_ARTERIAL_DIFF \\\n",
"count 293.000000 293.000000 293.0 \n",
"mean 0.931672 0.931672 -1.0 \n",
"std 0.114085 0.114085 0.0 \n",
"min -1.000000 -1.000000 -1.0 \n",
"25% 0.939394 0.939394 -1.0 \n",
"50% 0.939394 0.939394 -1.0 \n",
"75% 0.939394 0.939394 -1.0 \n",
"max 0.969697 0.969697 -1.0 \n",
"\n",
" SAT02_VENOUS_MEDIAN SAT02_VENOUS_MEAN SAT02_VENOUS_MIN \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.301416 0.301416 0.301416 \n",
"std 0.281543 0.281543 0.281543 \n",
"min -0.925926 -0.925926 -0.925926 \n",
"25% 0.345679 0.345679 0.345679 \n",
"50% 0.345679 0.345679 0.345679 \n",
"75% 0.345679 0.345679 0.345679 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" SAT02_VENOUS_MAX SAT02_VENOUS_DIFF SODIUM_MEDIAN SODIUM_MEAN \\\n",
"count 293.000000 293.0 293.000000 293.000000 \n",
"mean 0.301416 -1.0 -0.077133 -0.077133 \n",
"std 0.281543 0.0 0.198448 0.198448 \n",
"min -0.925926 -1.0 -0.714286 -0.714286 \n",
"25% 0.345679 -1.0 -0.171429 -0.171429 \n",
"50% 0.345679 -1.0 -0.028571 -0.028571 \n",
"75% 0.345679 -1.0 0.066667 0.066667 \n",
"max 1.000000 -1.0 1.000000 1.000000 \n",
"\n",
" SODIUM_MIN SODIUM_MAX SODIUM_DIFF TGO_MEDIAN TGO_MEAN \\\n",
"count 293.000000 293.000000 293.0 293.000000 293.000000 \n",
"mean -0.077133 -0.077133 -1.0 -0.993438 -0.993438 \n",
"std 0.198448 0.198448 0.0 0.019001 0.019001 \n",
"min -0.714286 -0.714286 -1.0 -0.999627 -0.999627 \n",
"25% -0.171429 -0.171429 -1.0 -0.996827 -0.996827 \n",
"50% -0.028571 -0.028571 -1.0 -0.995521 -0.995521 \n",
"75% 0.066667 0.066667 -1.0 -0.994588 -0.994588 \n",
"max 1.000000 1.000000 -1.0 -0.693944 -0.693944 \n",
"\n",
" TGO_MIN TGO_MAX TGO_DIFF TGP_MEDIAN TGP_MEAN TGP_MIN \\\n",
"count 293.000000 293.000000 293.0 293.000000 293.000000 293.000000 \n",
"mean -0.993438 -0.993438 -1.0 -0.986212 -0.986212 -0.986212 \n",
"std 0.019001 0.019001 0.0 0.015631 0.015631 0.015631 \n",
"min -0.999627 -0.999627 -1.0 -0.999619 -0.999619 -0.999619 \n",
"25% -0.996827 -0.996827 -1.0 -0.993521 -0.993521 -0.993521 \n",
"50% -0.995521 -0.995521 -1.0 -0.988567 -0.988567 -0.988567 \n",
"75% -0.994588 -0.994588 -1.0 -0.986280 -0.986280 -0.986280 \n",
"max -0.693944 -0.693944 -1.0 -0.794970 -0.794970 -0.794970 \n",
"\n",
" TGP_MAX TGP_DIFF TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"count 293.000000 293.0 293.000000 293.000000 293.000000 293.000000 \n",
"mean -0.986212 -1.0 -0.836803 -0.836803 -0.836803 -0.836803 \n",
"std 0.015631 0.0 0.042996 0.042996 0.042996 0.042996 \n",
"min -0.999619 -1.0 -0.961853 -0.961853 -0.961853 -0.961853 \n",
"25% -0.993521 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"50% -0.988567 -1.0 -0.846633 -0.846633 -0.846633 -0.846633 \n",
"75% -0.986280 -1.0 -0.836512 -0.836512 -0.836512 -0.836512 \n",
"max -0.794970 -1.0 -0.629428 -0.629428 -0.629428 -0.629428 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN UREA_MAX UREA_DIFF \\\n",
"count 293.0 293.000000 293.000000 293.000000 293.000000 293.0 \n",
"mean -1.0 -0.854512 -0.854512 -0.854512 -0.854512 -1.0 \n",
"std 0.0 0.075219 0.075219 0.075219 0.075219 0.0 \n",
"min -1.0 -0.978313 -0.978313 -0.978313 -0.978313 -1.0 \n",
"25% -1.0 -0.898795 -0.898795 -0.898795 -0.898795 -1.0 \n",
"50% -1.0 -0.874699 -0.874699 -0.874699 -0.874699 -1.0 \n",
"75% -1.0 -0.831325 -0.831325 -0.831325 -0.831325 -1.0 \n",
"max -1.0 -0.455422 -0.455422 -0.455422 -0.455422 -1.0 \n",
"\n",
" DIMER_MEDIAN DIMER_MEAN DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"count 293.000000 293.000000 293.000000 293.000000 293.0 \n",
"mean -0.958309 -0.958309 -0.958309 -0.958309 -1.0 \n",
"std 0.081395 0.081395 0.081395 0.081395 0.0 \n",
"min -1.000000 -1.000000 -1.000000 -1.000000 -1.0 \n",
"25% -0.984620 -0.984620 -0.984620 -0.984620 -1.0 \n",
"50% -0.978029 -0.978029 -0.978029 -0.978029 -1.0 \n",
"75% -0.966876 -0.966876 -0.966876 -0.966876 -1.0 \n",
"max -0.236287 -0.236287 -0.236287 -0.236287 -1.0 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN BLOODPRESSURE_SISTOLIC_MEAN \\\n",
"count 293.000000 293.000000 \n",
"mean -0.044813 -0.353956 \n",
"std 0.218544 0.235538 \n",
"min -0.654321 -0.880959 \n",
"25% -0.160494 -0.519963 \n",
"50% -0.044719 -0.384615 \n",
"75% 0.086420 -0.230605 \n",
"max 0.555556 0.476923 \n",
"\n",
" HEART_RATE_MEAN RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.246903 -0.502897 0.104200 \n",
"std 0.242663 0.155605 0.235370 \n",
"min -0.811321 -0.932203 -0.437500 \n",
"25% -0.405189 -0.577008 -0.080357 \n",
"50% -0.273192 -0.525424 0.071429 \n",
"75% -0.132075 -0.457627 0.248397 \n",
"max 0.660377 0.830508 0.964286 \n",
"\n",
" OXYGEN_SATURATION_MEAN BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"count 293.000000 293.000000 \n",
"mean 0.747809 -0.045815 \n",
"std 0.109896 0.221651 \n",
"min 0.315789 -0.679012 \n",
"25% 0.684211 -0.160494 \n",
"50% 0.744874 -0.037037 \n",
"75% 0.830409 0.086420 \n",
"max 1.000000 0.555556 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN \\\n",
"count 293.000000 293.000000 \n",
"mean -0.356395 -0.245879 \n",
"std 0.236684 0.252579 \n",
"min -0.884615 -0.811321 \n",
"25% -0.523077 -0.415094 \n",
"50% -0.384615 -0.270440 \n",
"75% -0.230769 -0.132075 \n",
"max 0.476923 0.962264 \n",
"\n",
" RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN OXYGEN_SATURATION_MEDIAN \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.495243 0.101170 0.751497 \n",
"std 0.159340 0.239179 0.113073 \n",
"min -0.931034 -0.464286 0.263158 \n",
"25% -0.568966 -0.089286 0.684211 \n",
"50% -0.517241 0.071429 0.754386 \n",
"75% -0.448276 0.250000 0.842105 \n",
"max 0.862069 0.964286 1.000000 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MIN BLOODPRESSURE_SISTOLIC_MIN \\\n",
"count 293.000000 293.000000 \n",
"mean 0.061275 -0.152414 \n",
"std 0.192080 0.200578 \n",
"min -0.515464 -0.587500 \n",
"25% -0.072165 -0.291667 \n",
"50% 0.030928 -0.175000 \n",
"75% 0.195876 -0.050000 \n",
"max 0.628866 0.575000 \n",
"\n",
" HEART_RATE_MIN RESPIRATORY_RATE_MIN TEMPERATURE_MIN \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.193791 -0.451487 0.396461 \n",
"std 0.229122 0.172518 0.159219 \n",
"min -0.803419 -0.857143 0.054945 \n",
"25% -0.341880 -0.547619 0.285714 \n",
"50% -0.222222 -0.500000 0.362637 \n",
"75% -0.094017 -0.404762 0.494505 \n",
"max 0.692308 1.000000 0.978022 \n",
"\n",
" OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"count 293.000000 293.000000 \n",
"mean 0.872175 -0.275483 \n",
"std 0.095568 0.175010 \n",
"min 0.040404 -0.760684 \n",
"25% 0.845118 -0.384615 \n",
"50% 0.885522 -0.270655 \n",
"75% 0.919192 -0.162393 \n",
"max 1.000000 0.350427 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX RESPIRATORY_RATE_MAX \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.491120 -0.339977 -0.506516 \n",
"std 0.176385 0.202344 0.156722 \n",
"min -0.891892 -0.850746 -0.939394 \n",
"25% -0.610811 -0.462687 -0.575758 \n",
"50% -0.513514 -0.358209 -0.515152 \n",
"75% -0.365766 -0.238806 -0.454545 \n",
"max 0.048649 0.552239 0.636364 \n",
"\n",
" TEMPERATURE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.024089 0.791974 -0.879784 \n",
"std 0.207794 0.110598 0.139821 \n",
"min -0.463768 0.315789 -1.000000 \n",
"25% -0.188406 0.736842 -1.000000 \n",
"50% -0.024155 0.789474 -0.913043 \n",
"75% 0.101449 0.877193 -0.785507 \n",
"max 0.768116 1.000000 -0.217391 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.886325 -0.876972 -0.913923 \n",
"std 0.128382 0.140858 0.105279 \n",
"min -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 \n",
"50% -0.926380 -0.893130 -0.941176 \n",
"75% -0.803681 -0.786260 -0.852941 \n",
"max -0.447853 0.160305 -0.323529 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF \\\n",
"count 293.000000 293.000000 \n",
"mean -0.877858 -0.952023 \n",
"std 0.134992 0.095003 \n",
"min -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 \n",
"50% -0.904762 -0.973064 \n",
"75% -0.785714 -0.939394 \n",
"max -0.238095 -0.090909 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF_REL BLOODPRESSURE_SISTOLIC_DIFF_REL \\\n",
"count 293.000000 293.000000 \n",
"mean -0.902344 -0.877666 \n",
"std 0.115414 0.136461 \n",
"min -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 \n",
"50% -0.928261 -0.918769 \n",
"75% -0.831621 -0.783383 \n",
"max -0.333333 -0.456376 \n",
"\n",
" HEART_RATE_DIFF_REL RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.909516 -0.911811 -0.878712 \n",
"std 0.098022 0.106116 0.133200 \n",
"min -1.000000 -1.000000 -1.000000 \n",
"25% -1.000000 -1.000000 -1.000000 \n",
"50% -0.920938 -0.939068 -0.908793 \n",
"75% -0.839287 -0.851852 -0.785121 \n",
"max -0.531749 -0.299283 -0.285468 \n",
"\n",
" OXYGEN_SATURATION_DIFF_REL AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean -0.952054 0.122867 0.129693 \n",
"std 0.094880 0.328846 0.336540 \n",
"min -1.000000 0.000000 0.000000 \n",
"25% -1.000000 0.000000 0.000000 \n",
"50% -0.973368 0.000000 0.000000 \n",
"75% -0.940644 0.000000 0.000000 \n",
"max -0.091698 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th AGE_PERCENTIL_50th \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.116041 0.112628 0.102389 \n",
"std 0.320822 0.316678 0.303678 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_60th AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.095563 0.102389 0.088737 \n",
"std 0.294494 0.303678 0.284851 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th ICU \n",
"count 293.000000 293.000000 293.000000 \n",
"mean 0.068259 0.061433 0.358362 \n",
"std 0.252622 0.240534 0.480340 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 "
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331
},
"id": "TwgYY2hO4Na8",
"outputId": "1972dd0d-c21b-4dea-ad3c-48f49b2015d3"
},
"source": [
"ICU_admission_distribution = final_data['ICU'].value_counts()\n",
"print(\"Total Patients after pre processing: \", sum(ICU_admission_distribution))\n",
"print(\"Distribution of ICU admissions\")\n",
"print(\"Patients who were not admitted to ICU: \",ICU_admission_distribution[0])\n",
"print(\"Patients who were admitted to ICU: \",ICU_admission_distribution[1])\n",
"labels= ['Admitted to ICU', 'Not Admitted to ICU']\n",
"colors=['tomato', 'deepskyblue']\n",
"sizes= [ICU_admission_distribution[1], ICU_admission_distribution[0]]\n",
"plt.pie(sizes,labels=labels, colors=colors, startangle=90, autopct='%1.1f%%')\n",
"plt.title(\"ICU Distribution of data\")\n",
"plt.axis('equal')\n",
"plt.show()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"Total Patients after pre processing: 293\n",
"Distribution of ICU admissions\n",
"Patients who were not admitted to ICU: 188\n",
"Patients who were admitted to ICU: 105\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 314
},
"id": "BlpxitpY09CT",
"outputId": "089d22b8-3aa7-4de7-b8e2-202a85cbe70a"
},
"source": [
"Age_distribution = final_data['AGE_ABOVE65'].value_counts()\n",
"print(\"Age Distribution\")\n",
"print(\"Patients below age 65: \",Age_distribution[0])\n",
"print(\"Patients above age 65: \",Age_distribution[1])\n",
"labels= ['Below 65', 'Above 65']\n",
"colors=['lightgreen', 'violet']\n",
"sizes= [Age_distribution[0], Age_distribution[1]]\n",
"plt.pie(sizes,labels=labels, colors=colors, startangle=90, autopct='%1.1f%%')\n",
"plt.axis('equal')\n",
"plt.title(\"Age Distribution of data\")\n",
"plt.show()\n",
"\n"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"Age Distribution\n",
"Patients below age 65: 172\n",
"Patients above age 65: 121\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 712
},
"id": "8l05_l_a4mKc",
"outputId": "f3b73fb5-d1bd-4535-ab10-50a7573db264"
},
"source": [
"ICU_Admitted_data = final_data[final_data['ICU']==1]\n",
"Age_distribution = ICU_Admitted_data['AGE_ABOVE65'].value_counts()\n",
"print(\"Age Distribution\")\n",
"print(\"Patients below age 65: \",Age_distribution[0])\n",
"print(\"Patients above age 65: \",Age_distribution[1])\n",
"labels= ['Below 65', 'Above 65']\n",
"colors=['orange', 'cyan']\n",
"sizes= [Age_distribution[0], Age_distribution[1]]\n",
"plt.pie(sizes,labels=labels, colors=colors, startangle=90, autopct='%1.1f%%')\n",
"plt.axis('equal')\n",
"plt.title(\"Age Distribution of ICU Admitted patients\")\n",
"plt.show()\n",
"\n",
"x = [[],[]]\n",
"x[0].append(final_data['AGE_PERCENTIL_10th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_20th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_30th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_40th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_50th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_60th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_70th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_80th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_90th'].value_counts()[1])\n",
"x[0].append(final_data['AGE_PERCENTIL_Above 90th'].value_counts()[1])\n",
"\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_10th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_20th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_30th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_40th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_50th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_60th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_70th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_80th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_90th'].value_counts()[1])\n",
"x[1].append(ICU_Admitted_data['AGE_PERCENTIL_Above 90th'].value_counts()[1])\n",
"\n",
"a = []\n",
"c=1\n",
"for i in x[0]:\n",
" a.extend([c*10]*i)\n",
" c+=1\n",
"plt.hist(a, 20, label='Total')\n",
"b = []\n",
"c=1\n",
"for i in x[1]:\n",
" b.extend([c*10]*i)\n",
" c+=1\n",
"print(x)\n",
"plt.hist(b, 20, label='ICU Admitted')\n",
"plt.xticks([10,20,30,40,50,60,70,80,90,100],['AGE_PERCENTIL_10th','AGE_PERCENTIL_20th','AGE_PERCENTIL_30th','AGE_PERCENTIL_40th','AGE_PERCENTIL_50th','AGE_PERCENTIL_60th','AGE_PERCENTIL_70th','AGE_PERCENTIL_80th','AGE_PERCENTIL_90th','AGE_PERCENTIL_Above 90'], rotation = 70)\n",
"plt.legend()\n",
"plt.ylabel('Frequency')\n",
"plt.title('Age Distribution Total and ICU Admitted')\n",
"plt.show()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"Age Distribution\n",
"Patients below age 65: 45\n",
"Patients above age 65: 60\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"[[36, 38, 34, 33, 30, 28, 30, 26, 20, 18], [6, 7, 11, 8, 13, 11, 13, 14, 12, 10]]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 410
},
"id": "Vhv-Hwzx7mCi",
"outputId": "e439380f-9308-489c-93c8-6076010d0650"
},
"source": [
"Diesease_Grouping_1 = final_data['DISEASE GROUPING 1'].value_counts()\n",
"Diesease_Grouping_2 = final_data['DISEASE GROUPING 2'].value_counts()\n",
"Diesease_Grouping_3 = final_data['DISEASE GROUPING 3'].value_counts()\n",
"Diesease_Grouping_4 = final_data['DISEASE GROUPING 4'].value_counts()\n",
"Diesease_Grouping_5 = final_data['DISEASE GROUPING 5'].value_counts()\n",
"Diesease_Grouping_6 = final_data['DISEASE GROUPING 6'].value_counts()\n",
"HTN_total = final_data['HTN'].value_counts()\n",
"Immunocompromised_total = final_data['IMMUNOCOMPROMISED'].value_counts()\n",
"Other_total = final_data['OTHER'].value_counts()\n",
"\n",
"ICU_Diesease_Grouping_1 = ICU_Admitted_data['DISEASE GROUPING 1'].value_counts()\n",
"ICU_Diesease_Grouping_2 = ICU_Admitted_data['DISEASE GROUPING 2'].value_counts()\n",
"ICU_Diesease_Grouping_3 = ICU_Admitted_data['DISEASE GROUPING 3'].value_counts()\n",
"ICU_Diesease_Grouping_4 = ICU_Admitted_data['DISEASE GROUPING 4'].value_counts()\n",
"ICU_Diesease_Grouping_5 = ICU_Admitted_data['DISEASE GROUPING 5'].value_counts()\n",
"ICU_Diesease_Grouping_6 = ICU_Admitted_data['DISEASE GROUPING 6'].value_counts()\n",
"HTN_ICU = ICU_Admitted_data['HTN'].value_counts()\n",
"Immunocompromised_ICU = ICU_Admitted_data['IMMUNOCOMPROMISED'].value_counts()\n",
"Other_ICU = ICU_Admitted_data['OTHER'].value_counts()\n",
"\n",
"x = np.array([[Diesease_Grouping_1[1],Diesease_Grouping_2[1],Diesease_Grouping_3[1],Diesease_Grouping_4[1],Diesease_Grouping_5[1],Diesease_Grouping_6[1],HTN_total[1], Immunocompromised_total[1]],[ICU_Diesease_Grouping_1[1],ICU_Diesease_Grouping_2[1],ICU_Diesease_Grouping_3[1],ICU_Diesease_Grouping_4[1],ICU_Diesease_Grouping_5[1],ICU_Diesease_Grouping_6[1],HTN_ICU[1], Immunocompromised_ICU[1]]])\n",
"a = []\n",
"c=1\n",
"for i in x[0]:\n",
" a.extend([c]*i)\n",
" c+=1\n",
"plt.hist(a, 15, label='Total')\n",
"b = []\n",
"c=1\n",
"for i in x[1]:\n",
" b.extend([c]*i)\n",
" c+=1\n",
"print(x)\n",
"plt.hist(b, 15, label='ICU Admitted')\n",
"plt.xticks([1,2,3,4,5,6,7,8,9],['Diesease_Grouping_1','Diesease_Grouping_2','Diesease_Grouping_3','Diesease_Grouping_4','Diesease_Grouping_5','Diesease_Grouping_6', 'Hypertension', 'Immunocompromised'], rotation = 70)\n",
"plt.legend()\n",
"plt.ylabel('Frequency')\n",
"plt.title('Disease Distribution Total and ICU Admitted')\n",
"plt.show()"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"[[28 7 25 6 34 14 50 48]\n",
" [12 5 14 5 17 5 26 22]]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "DWbzzfLoEN_B",
"outputId": "fdcb9332-42ca-47cc-f835-c27701992e97"
},
"source": [
"import seaborn as sns\n",
"corr = final_data.corr()\n",
"corr.shape\n",
"plt.subplots(figsize=(100,100))\n",
"ax = sns.heatmap(\n",
" corr, \n",
" vmin=-1, vmax=1, center=0,\n",
" cmap=sns.diverging_palette(20, 220, n=200),\n",
" square=True\n",
")\n",
"ax.set_xticklabels(\n",
" ax.get_xticklabels(),\n",
" rotation=90,\n",
" horizontalalignment='right'\n",
");\n",
"corr.tail()\n"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 1</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>DISEASE GROUPING 5</th>\n",
" <th>DISEASE GROUPING 6</th>\n",
" <th>HTN</th>\n",
" <th>IMMUNOCOMPROMISED</th>\n",
" <th>OTHER</th>\n",
" <th>ALBUMIN_MEDIAN</th>\n",
" <th>ALBUMIN_MEAN</th>\n",
" <th>ALBUMIN_MIN</th>\n",
" <th>ALBUMIN_MAX</th>\n",
" <th>ALBUMIN_DIFF</th>\n",
" <th>BE_ARTERIAL_MEDIAN</th>\n",
" <th>BE_ARTERIAL_MEAN</th>\n",
" <th>BE_ARTERIAL_MIN</th>\n",
" <th>BE_ARTERIAL_MAX</th>\n",
" <th>BE_ARTERIAL_DIFF</th>\n",
" <th>BE_VENOUS_MEDIAN</th>\n",
" <th>BE_VENOUS_MEAN</th>\n",
" <th>BE_VENOUS_MIN</th>\n",
" <th>BE_VENOUS_MAX</th>\n",
" <th>BE_VENOUS_DIFF</th>\n",
" <th>BIC_ARTERIAL_MEDIAN</th>\n",
" <th>BIC_ARTERIAL_MEAN</th>\n",
" <th>BIC_ARTERIAL_MIN</th>\n",
" <th>BIC_ARTERIAL_MAX</th>\n",
" <th>BIC_ARTERIAL_DIFF</th>\n",
" <th>BIC_VENOUS_MEDIAN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>BIC_VENOUS_MIN</th>\n",
" <th>BIC_VENOUS_MAX</th>\n",
" <th>BIC_VENOUS_DIFF</th>\n",
" <th>BILLIRUBIN_MEDIAN</th>\n",
" <th>BILLIRUBIN_MEAN</th>\n",
" <th>BILLIRUBIN_MIN</th>\n",
" <th>BILLIRUBIN_MAX</th>\n",
" <th>BILLIRUBIN_DIFF</th>\n",
" <th>BLAST_MEDIAN</th>\n",
" <th>BLAST_MEAN</th>\n",
" <th>BLAST_MIN</th>\n",
" <th>BLAST_MAX</th>\n",
" <th>BLAST_DIFF</th>\n",
" <th>CALCIUM_MEDIAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CALCIUM_MIN</th>\n",
" <th>CALCIUM_MAX</th>\n",
" <th>CALCIUM_DIFF</th>\n",
" <th>CREATININ_MEDIAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>CREATININ_MIN</th>\n",
" <th>CREATININ_MAX</th>\n",
" <th>CREATININ_DIFF</th>\n",
" <th>FFA_MEDIAN</th>\n",
" <th>FFA_MEAN</th>\n",
" <th>FFA_MIN</th>\n",
" <th>FFA_MAX</th>\n",
" <th>FFA_DIFF</th>\n",
" <th>GGT_MEDIAN</th>\n",
" <th>GGT_MEAN</th>\n",
" <th>GGT_MIN</th>\n",
" <th>GGT_MAX</th>\n",
" <th>GGT_DIFF</th>\n",
" <th>GLUCOSE_MEDIAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>GLUCOSE_MIN</th>\n",
" <th>GLUCOSE_MAX</th>\n",
" <th>GLUCOSE_DIFF</th>\n",
" <th>HEMATOCRITE_MEDIAN</th>\n",
" <th>HEMATOCRITE_MEAN</th>\n",
" <th>HEMATOCRITE_MIN</th>\n",
" <th>HEMATOCRITE_MAX</th>\n",
" <th>HEMATOCRITE_DIFF</th>\n",
" <th>HEMOGLOBIN_MEDIAN</th>\n",
" <th>HEMOGLOBIN_MEAN</th>\n",
" <th>HEMOGLOBIN_MIN</th>\n",
" <th>HEMOGLOBIN_MAX</th>\n",
" <th>HEMOGLOBIN_DIFF</th>\n",
" <th>INR_MEDIAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>INR_MIN</th>\n",
" <th>INR_MAX</th>\n",
" <th>INR_DIFF</th>\n",
" <th>LACTATE_MEDIAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LACTATE_MIN</th>\n",
" <th>LACTATE_MAX</th>\n",
" <th>LACTATE_DIFF</th>\n",
" <th>LEUKOCYTES_MEDIAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LEUKOCYTES_MIN</th>\n",
" <th>LEUKOCYTES_MAX</th>\n",
" <th>LEUKOCYTES_DIFF</th>\n",
" <th>LINFOCITOS_MEDIAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>LINFOCITOS_MIN</th>\n",
" <th>LINFOCITOS_MAX</th>\n",
" <th>LINFOCITOS_DIFF</th>\n",
" <th>NEUTROPHILES_MEDIAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>NEUTROPHILES_MIN</th>\n",
" <th>NEUTROPHILES_MAX</th>\n",
" <th>NEUTROPHILES_DIFF</th>\n",
" <th>P02_ARTERIAL_MEDIAN</th>\n",
" <th>P02_ARTERIAL_MEAN</th>\n",
" <th>P02_ARTERIAL_MIN</th>\n",
" <th>P02_ARTERIAL_MAX</th>\n",
" <th>P02_ARTERIAL_DIFF</th>\n",
" <th>P02_VENOUS_MEDIAN</th>\n",
" <th>P02_VENOUS_MEAN</th>\n",
" <th>P02_VENOUS_MIN</th>\n",
" <th>P02_VENOUS_MAX</th>\n",
" <th>P02_VENOUS_DIFF</th>\n",
" <th>PC02_ARTERIAL_MEDIAN</th>\n",
" <th>PC02_ARTERIAL_MEAN</th>\n",
" <th>PC02_ARTERIAL_MIN</th>\n",
" <th>PC02_ARTERIAL_MAX</th>\n",
" <th>PC02_ARTERIAL_DIFF</th>\n",
" <th>PC02_VENOUS_MEDIAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PC02_VENOUS_MIN</th>\n",
" <th>PC02_VENOUS_MAX</th>\n",
" <th>PC02_VENOUS_DIFF</th>\n",
" <th>PCR_MEDIAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PCR_MIN</th>\n",
" <th>PCR_MAX</th>\n",
" <th>PCR_DIFF</th>\n",
" <th>PH_ARTERIAL_MEDIAN</th>\n",
" <th>PH_ARTERIAL_MEAN</th>\n",
" <th>PH_ARTERIAL_MIN</th>\n",
" <th>PH_ARTERIAL_MAX</th>\n",
" <th>PH_ARTERIAL_DIFF</th>\n",
" <th>PH_VENOUS_MEDIAN</th>\n",
" <th>PH_VENOUS_MEAN</th>\n",
" <th>PH_VENOUS_MIN</th>\n",
" <th>PH_VENOUS_MAX</th>\n",
" <th>PH_VENOUS_DIFF</th>\n",
" <th>PLATELETS_MEDIAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>PLATELETS_MIN</th>\n",
" <th>PLATELETS_MAX</th>\n",
" <th>PLATELETS_DIFF</th>\n",
" <th>POTASSIUM_MEDIAN</th>\n",
" <th>POTASSIUM_MEAN</th>\n",
" <th>POTASSIUM_MIN</th>\n",
" <th>POTASSIUM_MAX</th>\n",
" <th>POTASSIUM_DIFF</th>\n",
" <th>SAT02_ARTERIAL_MEDIAN</th>\n",
" <th>SAT02_ARTERIAL_MEAN</th>\n",
" <th>SAT02_ARTERIAL_MIN</th>\n",
" <th>SAT02_ARTERIAL_MAX</th>\n",
" <th>SAT02_ARTERIAL_DIFF</th>\n",
" <th>SAT02_VENOUS_MEDIAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SAT02_VENOUS_MIN</th>\n",
" <th>SAT02_VENOUS_MAX</th>\n",
" <th>SAT02_VENOUS_DIFF</th>\n",
" <th>SODIUM_MEDIAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>SODIUM_MIN</th>\n",
" <th>SODIUM_MAX</th>\n",
" <th>SODIUM_DIFF</th>\n",
" <th>TGO_MEDIAN</th>\n",
" <th>TGO_MEAN</th>\n",
" <th>TGO_MIN</th>\n",
" <th>TGO_MAX</th>\n",
" <th>TGO_DIFF</th>\n",
" <th>TGP_MEDIAN</th>\n",
" <th>TGP_MEAN</th>\n",
" <th>TGP_MIN</th>\n",
" <th>TGP_MAX</th>\n",
" <th>TGP_DIFF</th>\n",
" <th>TTPA_MEDIAN</th>\n",
" <th>TTPA_MEAN</th>\n",
" <th>TTPA_MIN</th>\n",
" <th>TTPA_MAX</th>\n",
" <th>TTPA_DIFF</th>\n",
" <th>UREA_MEDIAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>UREA_MIN</th>\n",
" <th>UREA_MAX</th>\n",
" <th>UREA_DIFF</th>\n",
" <th>DIMER_MEDIAN</th>\n",
" <th>DIMER_MEAN</th>\n",
" <th>DIMER_MIN</th>\n",
" <th>DIMER_MAX</th>\n",
" <th>DIMER_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
" <th>HEART_RATE_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
" <th>HEART_RATE_MEDIAN</th>\n",
" <th>RESPIRATORY_RATE_MEDIAN</th>\n",
" <th>TEMPERATURE_MEDIAN</th>\n",
" <th>OXYGEN_SATURATION_MEDIAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>OXYGEN_SATURATION_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>RESPIRATORY_RATE_MAX</th>\n",
" <th>TEMPERATURE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
" <th>HEART_RATE_DIFF_REL</th>\n",
" <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
" <th>TEMPERATURE_DIFF_REL</th>\n",
" <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_30th</th>\n",
" <th>AGE_PERCENTIL_40th</th>\n",
" <th>AGE_PERCENTIL_50th</th>\n",
" <th>AGE_PERCENTIL_60th</th>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_70th</th>\n",
" <td>0.402675</td>\n",
" <td>0.081684</td>\n",
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" <td>0.138639</td>\n",
" <td>0.189637</td>\n",
" <td>0.053387</td>\n",
" <td>0.029902</td>\n",
" <td>0.116127</td>\n",
" <td>0.154684</td>\n",
" <td>0.049643</td>\n",
" <td>0.033136</td>\n",
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" <td>-0.109784</td>\n",
" <td>1.000000</td>\n",
" <td>-0.105393</td>\n",
" <td>-0.091415</td>\n",
" <td>-0.086408</td>\n",
" <td>0.052805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <td>0.372051</td>\n",
" <td>0.061864</td>\n",
" <td>-0.048820</td>\n",
" <td>-0.052347</td>\n",
" <td>0.039624</td>\n",
" <td>0.111784</td>\n",
" <td>-0.069903</td>\n",
" <td>0.081773</td>\n",
" <td>-0.008411</td>\n",
" <td>-0.031463</td>\n",
" <td>0.001602</td>\n",
" <td>0.001602</td>\n",
" <td>0.001602</td>\n",
" <td>0.001602</td>\n",
" <td>NaN</td>\n",
" <td>-0.033199</td>\n",
" <td>-0.033199</td>\n",
" <td>-0.033199</td>\n",
" <td>-0.033199</td>\n",
" <td>NaN</td>\n",
" <td>0.036012</td>\n",
" <td>0.036012</td>\n",
" <td>0.036012</td>\n",
" <td>0.036012</td>\n",
" <td>NaN</td>\n",
" <td>-0.012301</td>\n",
" <td>-0.012301</td>\n",
" <td>-0.012301</td>\n",
" <td>-0.012301</td>\n",
" <td>NaN</td>\n",
" <td>0.031392</td>\n",
" <td>0.031392</td>\n",
" <td>0.031392</td>\n",
" <td>0.031392</td>\n",
" <td>NaN</td>\n",
" <td>0.023247</td>\n",
" <td>0.023247</td>\n",
" <td>0.023247</td>\n",
" <td>0.023247</td>\n",
" <td>NaN</td>\n",
" <td>-0.020476</td>\n",
" <td>-0.020476</td>\n",
" <td>-0.020476</td>\n",
" <td>-0.020476</td>\n",
" <td>NaN</td>\n",
" <td>-0.106084</td>\n",
" <td>-0.106084</td>\n",
" <td>-0.106084</td>\n",
" <td>-0.106084</td>\n",
" <td>NaN</td>\n",
" <td>0.187437</td>\n",
" <td>0.187437</td>\n",
" <td>0.187437</td>\n",
" <td>0.187437</td>\n",
" <td>NaN</td>\n",
" <td>0.028170</td>\n",
" <td>0.028170</td>\n",
" <td>0.028170</td>\n",
" <td>0.028170</td>\n",
" <td>NaN</td>\n",
" <td>-0.030858</td>\n",
" <td>-0.030858</td>\n",
" <td>-0.030858</td>\n",
" <td>-0.030858</td>\n",
" <td>NaN</td>\n",
" <td>-0.006491</td>\n",
" <td>-0.006491</td>\n",
" <td>-0.006491</td>\n",
" <td>-0.006491</td>\n",
" <td>NaN</td>\n",
" <td>-0.050288</td>\n",
" <td>-0.050288</td>\n",
" <td>-0.050288</td>\n",
" <td>-0.050288</td>\n",
" <td>NaN</td>\n",
" <td>-0.044214</td>\n",
" <td>-0.044214</td>\n",
" <td>-0.044214</td>\n",
" <td>-0.044214</td>\n",
" <td>NaN</td>\n",
" <td>0.066156</td>\n",
" <td>0.066156</td>\n",
" <td>0.066156</td>\n",
" <td>0.066156</td>\n",
" <td>NaN</td>\n",
" <td>0.072293</td>\n",
" <td>0.072293</td>\n",
" <td>0.072293</td>\n",
" <td>0.072293</td>\n",
" <td>NaN</td>\n",
" <td>-0.045179</td>\n",
" <td>-0.045179</td>\n",
" <td>-0.045179</td>\n",
" <td>-0.045179</td>\n",
" <td>NaN</td>\n",
" <td>-0.124717</td>\n",
" <td>-0.124717</td>\n",
" <td>-0.124717</td>\n",
" <td>-0.124717</td>\n",
" <td>NaN</td>\n",
" <td>-0.017195</td>\n",
" <td>-0.017195</td>\n",
" <td>-0.017195</td>\n",
" <td>-0.017195</td>\n",
" <td>NaN</td>\n",
" <td>0.016702</td>\n",
" <td>0.016702</td>\n",
" <td>0.016702</td>\n",
" <td>0.016702</td>\n",
" <td>NaN</td>\n",
" <td>0.008631</td>\n",
" <td>0.008631</td>\n",
" <td>0.008631</td>\n",
" <td>0.008631</td>\n",
" <td>NaN</td>\n",
" <td>0.037350</td>\n",
" <td>0.037350</td>\n",
" <td>0.037350</td>\n",
" <td>0.037350</td>\n",
" <td>NaN</td>\n",
" <td>-0.038268</td>\n",
" <td>-0.038268</td>\n",
" <td>-0.038268</td>\n",
" <td>-0.038268</td>\n",
" <td>NaN</td>\n",
" <td>-0.008421</td>\n",
" <td>-0.008421</td>\n",
" <td>-0.008421</td>\n",
" <td>-0.008421</td>\n",
" <td>NaN</td>\n",
" <td>-0.035351</td>\n",
" <td>-0.035351</td>\n",
" <td>-0.035351</td>\n",
" <td>-0.035351</td>\n",
" <td>NaN</td>\n",
" <td>0.097817</td>\n",
" <td>0.097817</td>\n",
" <td>0.097817</td>\n",
" <td>0.097817</td>\n",
" <td>NaN</td>\n",
" <td>-0.176491</td>\n",
" <td>-0.176491</td>\n",
" <td>-0.176491</td>\n",
" <td>-0.176491</td>\n",
" <td>NaN</td>\n",
" <td>-0.119943</td>\n",
" <td>-0.119943</td>\n",
" <td>-0.119943</td>\n",
" <td>-0.119943</td>\n",
" <td>NaN</td>\n",
" <td>0.021159</td>\n",
" <td>0.021159</td>\n",
" <td>0.021159</td>\n",
" <td>0.021159</td>\n",
" <td>NaN</td>\n",
" <td>0.062060</td>\n",
" <td>0.062060</td>\n",
" <td>0.062060</td>\n",
" <td>0.062060</td>\n",
" <td>NaN</td>\n",
" <td>-0.004862</td>\n",
" <td>-0.004862</td>\n",
" <td>-0.004862</td>\n",
" <td>-0.004862</td>\n",
" <td>NaN</td>\n",
" <td>0.166311</td>\n",
" <td>0.166311</td>\n",
" <td>0.166311</td>\n",
" <td>0.166311</td>\n",
" <td>NaN</td>\n",
" <td>0.073675</td>\n",
" <td>0.073675</td>\n",
" <td>0.073675</td>\n",
" <td>0.073675</td>\n",
" <td>NaN</td>\n",
" <td>-0.090077</td>\n",
" <td>-0.090077</td>\n",
" <td>-0.090077</td>\n",
" <td>-0.090077</td>\n",
" <td>NaN</td>\n",
" <td>0.246949</td>\n",
" <td>0.246949</td>\n",
" <td>0.246949</td>\n",
" <td>0.246949</td>\n",
" <td>NaN</td>\n",
" <td>0.066066</td>\n",
" <td>0.066066</td>\n",
" <td>0.066066</td>\n",
" <td>0.066066</td>\n",
" <td>NaN</td>\n",
" <td>-0.010905</td>\n",
" <td>0.065313</td>\n",
" <td>-0.090179</td>\n",
" <td>0.008452</td>\n",
" <td>-0.009846</td>\n",
" <td>-0.060254</td>\n",
" <td>-0.011728</td>\n",
" <td>0.073211</td>\n",
" <td>-0.063178</td>\n",
" <td>0.012784</td>\n",
" <td>-0.009997</td>\n",
" <td>-0.050306</td>\n",
" <td>0.016969</td>\n",
" <td>0.073879</td>\n",
" <td>-0.095074</td>\n",
" <td>0.017877</td>\n",
" <td>0.015329</td>\n",
" <td>0.011042</td>\n",
" <td>-0.057432</td>\n",
" <td>0.036275</td>\n",
" <td>-0.112635</td>\n",
" <td>-0.016837</td>\n",
" <td>-0.033501</td>\n",
" <td>-0.093839</td>\n",
" <td>-0.092799</td>\n",
" <td>-0.056737</td>\n",
" <td>-0.027386</td>\n",
" <td>-0.048452</td>\n",
" <td>-0.061946</td>\n",
" <td>-0.053039</td>\n",
" <td>-0.090933</td>\n",
" <td>-0.070643</td>\n",
" <td>-0.063098</td>\n",
" <td>-0.054256</td>\n",
" <td>-0.062087</td>\n",
" <td>-0.053039</td>\n",
" <td>-0.116793</td>\n",
" <td>-0.120463</td>\n",
" <td>-0.113063</td>\n",
" <td>-0.111174</td>\n",
" <td>-0.105393</td>\n",
" <td>-0.101435</td>\n",
" <td>-0.105393</td>\n",
" <td>1.000000</td>\n",
" <td>-0.084463</td>\n",
" <td>-0.079836</td>\n",
" <td>0.117203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <td>0.322705</td>\n",
" <td>0.050118</td>\n",
" <td>0.134898</td>\n",
" <td>0.062662</td>\n",
" <td>-0.039135</td>\n",
" <td>0.113210</td>\n",
" <td>-0.060631</td>\n",
" <td>0.093065</td>\n",
" <td>0.099587</td>\n",
" <td>-0.006109</td>\n",
" <td>0.013411</td>\n",
" <td>0.013411</td>\n",
" <td>0.013411</td>\n",
" <td>0.013411</td>\n",
" <td>NaN</td>\n",
" <td>0.008328</td>\n",
" <td>0.008328</td>\n",
" <td>0.008328</td>\n",
" <td>0.008328</td>\n",
" <td>NaN</td>\n",
" <td>0.001137</td>\n",
" <td>0.001137</td>\n",
" <td>0.001137</td>\n",
" <td>0.001137</td>\n",
" <td>NaN</td>\n",
" <td>-0.135710</td>\n",
" <td>-0.135710</td>\n",
" <td>-0.135710</td>\n",
" <td>-0.135710</td>\n",
" <td>NaN</td>\n",
" <td>-0.023979</td>\n",
" <td>-0.023979</td>\n",
" <td>-0.023979</td>\n",
" <td>-0.023979</td>\n",
" <td>NaN</td>\n",
" <td>0.012834</td>\n",
" <td>0.012834</td>\n",
" <td>0.012834</td>\n",
" <td>0.012834</td>\n",
" <td>NaN</td>\n",
" <td>0.212447</td>\n",
" <td>0.212447</td>\n",
" <td>0.212447</td>\n",
" <td>0.212447</td>\n",
" <td>NaN</td>\n",
" <td>-0.008451</td>\n",
" <td>-0.008451</td>\n",
" <td>-0.008451</td>\n",
" <td>-0.008451</td>\n",
" <td>NaN</td>\n",
" <td>0.011321</td>\n",
" <td>0.011321</td>\n",
" <td>0.011321</td>\n",
" <td>0.011321</td>\n",
" <td>NaN</td>\n",
" <td>0.018527</td>\n",
" <td>0.018527</td>\n",
" <td>0.018527</td>\n",
" <td>0.018527</td>\n",
" <td>NaN</td>\n",
" <td>-0.041682</td>\n",
" <td>-0.041682</td>\n",
" <td>-0.041682</td>\n",
" <td>-0.041682</td>\n",
" <td>NaN</td>\n",
" <td>-0.046688</td>\n",
" <td>-0.046688</td>\n",
" <td>-0.046688</td>\n",
" <td>-0.046688</td>\n",
" <td>NaN</td>\n",
" <td>-0.075065</td>\n",
" <td>-0.075065</td>\n",
" <td>-0.075065</td>\n",
" <td>-0.075065</td>\n",
" <td>NaN</td>\n",
" <td>-0.092330</td>\n",
" <td>-0.092330</td>\n",
" <td>-0.092330</td>\n",
" <td>-0.092330</td>\n",
" <td>NaN</td>\n",
" <td>0.003333</td>\n",
" <td>0.003333</td>\n",
" <td>0.003333</td>\n",
" <td>0.003333</td>\n",
" <td>NaN</td>\n",
" <td>-0.157023</td>\n",
" <td>-0.157023</td>\n",
" <td>-0.157023</td>\n",
" <td>-0.157023</td>\n",
" <td>NaN</td>\n",
" <td>0.067807</td>\n",
" <td>0.067807</td>\n",
" <td>0.067807</td>\n",
" <td>0.067807</td>\n",
" <td>NaN</td>\n",
" <td>-0.024487</td>\n",
" <td>-0.024487</td>\n",
" <td>-0.024487</td>\n",
" <td>-0.024487</td>\n",
" <td>NaN</td>\n",
" <td>0.068433</td>\n",
" <td>0.068433</td>\n",
" <td>0.068433</td>\n",
" <td>0.068433</td>\n",
" <td>NaN</td>\n",
" <td>0.006427</td>\n",
" <td>0.006427</td>\n",
" <td>0.006427</td>\n",
" <td>0.006427</td>\n",
" <td>NaN</td>\n",
" <td>-0.026206</td>\n",
" <td>-0.026206</td>\n",
" <td>-0.026206</td>\n",
" <td>-0.026206</td>\n",
" <td>NaN</td>\n",
" <td>-0.157444</td>\n",
" <td>-0.157444</td>\n",
" <td>-0.157444</td>\n",
" <td>-0.157444</td>\n",
" <td>NaN</td>\n",
" <td>-0.038205</td>\n",
" <td>-0.038205</td>\n",
" <td>-0.038205</td>\n",
" <td>-0.038205</td>\n",
" <td>NaN</td>\n",
" <td>0.011468</td>\n",
" <td>0.011468</td>\n",
" <td>0.011468</td>\n",
" <td>0.011468</td>\n",
" <td>NaN</td>\n",
" <td>0.051011</td>\n",
" <td>0.051011</td>\n",
" <td>0.051011</td>\n",
" <td>0.051011</td>\n",
" <td>NaN</td>\n",
" <td>0.033351</td>\n",
" <td>0.033351</td>\n",
" <td>0.033351</td>\n",
" <td>0.033351</td>\n",
" <td>NaN</td>\n",
" <td>-0.031250</td>\n",
" <td>-0.031250</td>\n",
" <td>-0.031250</td>\n",
" <td>-0.031250</td>\n",
" <td>NaN</td>\n",
" <td>0.134179</td>\n",
" <td>0.134179</td>\n",
" <td>0.134179</td>\n",
" <td>0.134179</td>\n",
" <td>NaN</td>\n",
" <td>0.012351</td>\n",
" <td>0.012351</td>\n",
" <td>0.012351</td>\n",
" <td>0.012351</td>\n",
" <td>NaN</td>\n",
" <td>-0.093800</td>\n",
" <td>-0.093800</td>\n",
" <td>-0.093800</td>\n",
" <td>-0.093800</td>\n",
" <td>NaN</td>\n",
" <td>-0.052278</td>\n",
" <td>-0.052278</td>\n",
" <td>-0.052278</td>\n",
" <td>-0.052278</td>\n",
" <td>NaN</td>\n",
" <td>-0.032935</td>\n",
" <td>-0.032935</td>\n",
" <td>-0.032935</td>\n",
" <td>-0.032935</td>\n",
" <td>NaN</td>\n",
" <td>-0.045916</td>\n",
" <td>-0.045916</td>\n",
" <td>-0.045916</td>\n",
" <td>-0.045916</td>\n",
" <td>NaN</td>\n",
" <td>0.052318</td>\n",
" <td>0.052318</td>\n",
" <td>0.052318</td>\n",
" <td>0.052318</td>\n",
" <td>NaN</td>\n",
" <td>0.065191</td>\n",
" <td>0.065191</td>\n",
" <td>0.065191</td>\n",
" <td>0.065191</td>\n",
" <td>NaN</td>\n",
" <td>-0.007696</td>\n",
" <td>-0.007696</td>\n",
" <td>-0.007696</td>\n",
" <td>-0.007696</td>\n",
" <td>NaN</td>\n",
" <td>0.035194</td>\n",
" <td>0.213388</td>\n",
" <td>-0.042347</td>\n",
" <td>0.020020</td>\n",
" <td>0.006909</td>\n",
" <td>-0.033732</td>\n",
" <td>0.032509</td>\n",
" <td>0.212713</td>\n",
" <td>-0.037758</td>\n",
" <td>0.020266</td>\n",
" <td>0.017651</td>\n",
" <td>-0.036191</td>\n",
" <td>0.070790</td>\n",
" <td>0.227708</td>\n",
" <td>0.005969</td>\n",
" <td>0.028998</td>\n",
" <td>0.042049</td>\n",
" <td>-0.013238</td>\n",
" <td>-0.006645</td>\n",
" <td>0.173577</td>\n",
" <td>-0.087677</td>\n",
" <td>-0.025425</td>\n",
" <td>-0.055713</td>\n",
" <td>-0.093220</td>\n",
" <td>-0.090489</td>\n",
" <td>-0.078546</td>\n",
" <td>-0.137506</td>\n",
" <td>-0.075868</td>\n",
" <td>-0.124175</td>\n",
" <td>-0.028338</td>\n",
" <td>-0.085449</td>\n",
" <td>-0.081142</td>\n",
" <td>-0.147246</td>\n",
" <td>-0.075906</td>\n",
" <td>-0.125159</td>\n",
" <td>-0.029484</td>\n",
" <td>-0.101302</td>\n",
" <td>-0.104485</td>\n",
" <td>-0.098067</td>\n",
" <td>-0.096428</td>\n",
" <td>-0.091415</td>\n",
" <td>-0.087981</td>\n",
" <td>-0.091415</td>\n",
" <td>-0.084463</td>\n",
" <td>1.000000</td>\n",
" <td>-0.069247</td>\n",
" <td>0.136393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_Above 90th</th>\n",
" <td>0.305029</td>\n",
" <td>0.303608</td>\n",
" <td>0.053050</td>\n",
" <td>0.074493</td>\n",
" <td>-0.036992</td>\n",
" <td>0.262335</td>\n",
" <td>0.009324</td>\n",
" <td>0.148417</td>\n",
" <td>0.078771</td>\n",
" <td>0.124363</td>\n",
" <td>-0.156210</td>\n",
" <td>-0.156210</td>\n",
" <td>-0.156210</td>\n",
" <td>-0.156210</td>\n",
" <td>NaN</td>\n",
" <td>-0.027218</td>\n",
" <td>-0.027218</td>\n",
" <td>-0.027218</td>\n",
" <td>-0.027218</td>\n",
" <td>NaN</td>\n",
" <td>0.068960</td>\n",
" <td>0.068960</td>\n",
" <td>0.068960</td>\n",
" <td>0.068960</td>\n",
" <td>NaN</td>\n",
" <td>-0.010085</td>\n",
" <td>-0.010085</td>\n",
" <td>-0.010085</td>\n",
" <td>-0.010085</td>\n",
" <td>NaN</td>\n",
" <td>-0.026674</td>\n",
" <td>-0.026674</td>\n",
" <td>-0.026674</td>\n",
" <td>-0.026674</td>\n",
" <td>NaN</td>\n",
" <td>-0.013805</td>\n",
" <td>-0.013805</td>\n",
" <td>-0.013805</td>\n",
" <td>-0.013805</td>\n",
" <td>NaN</td>\n",
" <td>-0.016787</td>\n",
" <td>-0.016787</td>\n",
" <td>-0.016787</td>\n",
" <td>-0.016787</td>\n",
" <td>NaN</td>\n",
" <td>-0.008727</td>\n",
" <td>-0.008727</td>\n",
" <td>-0.008727</td>\n",
" <td>-0.008727</td>\n",
" <td>NaN</td>\n",
" <td>0.048913</td>\n",
" <td>0.048913</td>\n",
" <td>0.048913</td>\n",
" <td>0.048913</td>\n",
" <td>NaN</td>\n",
" <td>-0.051167</td>\n",
" <td>-0.051167</td>\n",
" <td>-0.051167</td>\n",
" <td>-0.051167</td>\n",
" <td>NaN</td>\n",
" <td>-0.064201</td>\n",
" <td>-0.064201</td>\n",
" <td>-0.064201</td>\n",
" <td>-0.064201</td>\n",
" <td>NaN</td>\n",
" <td>0.182894</td>\n",
" <td>0.182894</td>\n",
" <td>0.182894</td>\n",
" <td>0.182894</td>\n",
" <td>NaN</td>\n",
" <td>-0.168841</td>\n",
" <td>-0.168841</td>\n",
" <td>-0.168841</td>\n",
" <td>-0.168841</td>\n",
" <td>NaN</td>\n",
" <td>-0.184098</td>\n",
" <td>-0.184098</td>\n",
" <td>-0.184098</td>\n",
" <td>-0.184098</td>\n",
" <td>NaN</td>\n",
" <td>-0.019227</td>\n",
" <td>-0.019227</td>\n",
" <td>-0.019227</td>\n",
" <td>-0.019227</td>\n",
" <td>NaN</td>\n",
" <td>0.002241</td>\n",
" <td>0.002241</td>\n",
" <td>0.002241</td>\n",
" <td>0.002241</td>\n",
" <td>NaN</td>\n",
" <td>0.059318</td>\n",
" <td>0.059318</td>\n",
" <td>0.059318</td>\n",
" <td>0.059318</td>\n",
" <td>NaN</td>\n",
" <td>-0.062136</td>\n",
" <td>-0.062136</td>\n",
" <td>-0.062136</td>\n",
" <td>-0.062136</td>\n",
" <td>NaN</td>\n",
" <td>0.062926</td>\n",
" <td>0.062926</td>\n",
" <td>0.062926</td>\n",
" <td>0.062926</td>\n",
" <td>NaN</td>\n",
" <td>0.013694</td>\n",
" <td>0.013694</td>\n",
" <td>0.013694</td>\n",
" <td>0.013694</td>\n",
" <td>NaN</td>\n",
" <td>0.107393</td>\n",
" <td>0.107393</td>\n",
" <td>0.107393</td>\n",
" <td>0.107393</td>\n",
" <td>NaN</td>\n",
" <td>0.030621</td>\n",
" <td>0.030621</td>\n",
" <td>0.030621</td>\n",
" <td>0.030621</td>\n",
" <td>NaN</td>\n",
" <td>0.035268</td>\n",
" <td>0.035268</td>\n",
" <td>0.035268</td>\n",
" <td>0.035268</td>\n",
" <td>NaN</td>\n",
" <td>-0.058987</td>\n",
" <td>-0.058987</td>\n",
" <td>-0.058987</td>\n",
" <td>-0.058987</td>\n",
" <td>NaN</td>\n",
" <td>-0.028983</td>\n",
" <td>-0.028983</td>\n",
" <td>-0.028983</td>\n",
" <td>-0.028983</td>\n",
" <td>NaN</td>\n",
" <td>-0.098350</td>\n",
" <td>-0.098350</td>\n",
" <td>-0.098350</td>\n",
" <td>-0.098350</td>\n",
" <td>NaN</td>\n",
" <td>-0.028189</td>\n",
" <td>-0.028189</td>\n",
" <td>-0.028189</td>\n",
" <td>-0.028189</td>\n",
" <td>NaN</td>\n",
" <td>0.138645</td>\n",
" <td>0.138645</td>\n",
" <td>0.138645</td>\n",
" <td>0.138645</td>\n",
" <td>NaN</td>\n",
" <td>0.017347</td>\n",
" <td>0.017347</td>\n",
" <td>0.017347</td>\n",
" <td>0.017347</td>\n",
" <td>NaN</td>\n",
" <td>-0.035564</td>\n",
" <td>-0.035564</td>\n",
" <td>-0.035564</td>\n",
" <td>-0.035564</td>\n",
" <td>NaN</td>\n",
" <td>-0.078044</td>\n",
" <td>-0.078044</td>\n",
" <td>-0.078044</td>\n",
" <td>-0.078044</td>\n",
" <td>NaN</td>\n",
" <td>-0.035721</td>\n",
" <td>-0.035721</td>\n",
" <td>-0.035721</td>\n",
" <td>-0.035721</td>\n",
" <td>NaN</td>\n",
" <td>-0.103004</td>\n",
" <td>-0.103004</td>\n",
" <td>-0.103004</td>\n",
" <td>-0.103004</td>\n",
" <td>NaN</td>\n",
" <td>-0.067098</td>\n",
" <td>-0.067098</td>\n",
" <td>-0.067098</td>\n",
" <td>-0.067098</td>\n",
" <td>NaN</td>\n",
" <td>0.236355</td>\n",
" <td>0.236355</td>\n",
" <td>0.236355</td>\n",
" <td>0.236355</td>\n",
" <td>NaN</td>\n",
" <td>0.075485</td>\n",
" <td>0.075485</td>\n",
" <td>0.075485</td>\n",
" <td>0.075485</td>\n",
" <td>NaN</td>\n",
" <td>-0.148409</td>\n",
" <td>0.055248</td>\n",
" <td>0.002933</td>\n",
" <td>0.152784</td>\n",
" <td>0.016918</td>\n",
" <td>0.008539</td>\n",
" <td>-0.144490</td>\n",
" <td>0.049881</td>\n",
" <td>0.003529</td>\n",
" <td>0.156166</td>\n",
" <td>0.015435</td>\n",
" <td>0.009033</td>\n",
" <td>-0.118053</td>\n",
" <td>0.064086</td>\n",
" <td>0.024762</td>\n",
" <td>0.170116</td>\n",
" <td>0.029084</td>\n",
" <td>0.045574</td>\n",
" <td>-0.140688</td>\n",
" <td>0.060473</td>\n",
" <td>-0.016091</td>\n",
" <td>0.112055</td>\n",
" <td>0.006042</td>\n",
" <td>-0.018779</td>\n",
" <td>-0.042366</td>\n",
" <td>-0.003984</td>\n",
" <td>-0.059618</td>\n",
" <td>-0.067668</td>\n",
" <td>-0.029522</td>\n",
" <td>-0.054236</td>\n",
" <td>-0.014615</td>\n",
" <td>-0.007365</td>\n",
" <td>-0.058710</td>\n",
" <td>-0.071135</td>\n",
" <td>-0.029038</td>\n",
" <td>-0.053933</td>\n",
" <td>-0.095753</td>\n",
" <td>-0.098762</td>\n",
" <td>-0.092696</td>\n",
" <td>-0.091147</td>\n",
" <td>-0.086408</td>\n",
" <td>-0.083162</td>\n",
" <td>-0.086408</td>\n",
" <td>-0.079836</td>\n",
" <td>-0.069247</td>\n",
" <td>1.000000</td>\n",
" <td>0.105210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ICU</th>\n",
" <td>0.240516</td>\n",
" <td>0.047593</td>\n",
" <td>0.116123</td>\n",
" <td>0.128431</td>\n",
" <td>0.143218</td>\n",
" <td>0.107020</td>\n",
" <td>-0.000569</td>\n",
" <td>0.152904</td>\n",
" <td>0.092280</td>\n",
" <td>-0.016866</td>\n",
" <td>-0.051499</td>\n",
" <td>-0.051499</td>\n",
" <td>-0.051499</td>\n",
" <td>-0.051499</td>\n",
" <td>NaN</td>\n",
" <td>0.090883</td>\n",
" <td>0.090883</td>\n",
" <td>0.090883</td>\n",
" <td>0.090883</td>\n",
" <td>NaN</td>\n",
" <td>0.071562</td>\n",
" <td>0.071562</td>\n",
" <td>0.071562</td>\n",
" <td>0.071562</td>\n",
" <td>NaN</td>\n",
" <td>0.069185</td>\n",
" <td>0.069185</td>\n",
" <td>0.069185</td>\n",
" <td>0.069185</td>\n",
" <td>NaN</td>\n",
" <td>-0.128091</td>\n",
" <td>-0.128091</td>\n",
" <td>-0.128091</td>\n",
" <td>-0.128091</td>\n",
" <td>NaN</td>\n",
" <td>0.106479</td>\n",
" <td>0.106479</td>\n",
" <td>0.106479</td>\n",
" <td>0.106479</td>\n",
" <td>NaN</td>\n",
" <td>0.087800</td>\n",
" <td>0.087800</td>\n",
" <td>0.087800</td>\n",
" <td>0.087800</td>\n",
" <td>NaN</td>\n",
" <td>-0.281260</td>\n",
" <td>-0.281260</td>\n",
" <td>-0.281260</td>\n",
" <td>-0.281260</td>\n",
" <td>NaN</td>\n",
" <td>0.219298</td>\n",
" <td>0.219298</td>\n",
" <td>0.219298</td>\n",
" <td>0.219298</td>\n",
" <td>NaN</td>\n",
" <td>0.101017</td>\n",
" <td>0.101017</td>\n",
" <td>0.101017</td>\n",
" <td>0.101017</td>\n",
" <td>NaN</td>\n",
" <td>0.075144</td>\n",
" <td>0.075144</td>\n",
" <td>0.075144</td>\n",
" <td>0.075144</td>\n",
" <td>NaN</td>\n",
" <td>0.186633</td>\n",
" <td>0.186633</td>\n",
" <td>0.186633</td>\n",
" <td>0.186633</td>\n",
" <td>NaN</td>\n",
" <td>-0.064099</td>\n",
" <td>-0.064099</td>\n",
" <td>-0.064099</td>\n",
" <td>-0.064099</td>\n",
" <td>NaN</td>\n",
" <td>-0.069985</td>\n",
" <td>-0.069985</td>\n",
" <td>-0.069985</td>\n",
" <td>-0.069985</td>\n",
" <td>NaN</td>\n",
" <td>0.125135</td>\n",
" <td>0.125135</td>\n",
" <td>0.125135</td>\n",
" <td>0.125135</td>\n",
" <td>NaN</td>\n",
" <td>-0.206652</td>\n",
" <td>-0.206652</td>\n",
" <td>-0.206652</td>\n",
" <td>-0.206652</td>\n",
" <td>NaN</td>\n",
" <td>0.116605</td>\n",
" <td>0.116605</td>\n",
" <td>0.116605</td>\n",
" <td>0.116605</td>\n",
" <td>NaN</td>\n",
" <td>-0.257798</td>\n",
" <td>-0.257798</td>\n",
" <td>-0.257798</td>\n",
" <td>-0.257798</td>\n",
" <td>NaN</td>\n",
" <td>0.196802</td>\n",
" <td>0.196802</td>\n",
" <td>0.196802</td>\n",
" <td>0.196802</td>\n",
" <td>NaN</td>\n",
" <td>-0.034177</td>\n",
" <td>-0.034177</td>\n",
" <td>-0.034177</td>\n",
" <td>-0.034177</td>\n",
" <td>NaN</td>\n",
" <td>-0.033937</td>\n",
" <td>-0.033937</td>\n",
" <td>-0.033937</td>\n",
" <td>-0.033937</td>\n",
" <td>NaN</td>\n",
" <td>-0.025753</td>\n",
" <td>-0.025753</td>\n",
" <td>-0.025753</td>\n",
" <td>-0.025753</td>\n",
" <td>NaN</td>\n",
" <td>-0.151272</td>\n",
" <td>-0.151272</td>\n",
" <td>-0.151272</td>\n",
" <td>-0.151272</td>\n",
" <td>NaN</td>\n",
" <td>0.342681</td>\n",
" <td>0.342681</td>\n",
" <td>0.342681</td>\n",
" <td>0.342681</td>\n",
" <td>NaN</td>\n",
" <td>0.044200</td>\n",
" <td>0.044200</td>\n",
" <td>0.044200</td>\n",
" <td>0.044200</td>\n",
" <td>NaN</td>\n",
" <td>0.052446</td>\n",
" <td>0.052446</td>\n",
" <td>0.052446</td>\n",
" <td>0.052446</td>\n",
" <td>NaN</td>\n",
" <td>-0.205424</td>\n",
" <td>-0.205424</td>\n",
" <td>-0.205424</td>\n",
" <td>-0.205424</td>\n",
" <td>NaN</td>\n",
" <td>0.066486</td>\n",
" <td>0.066486</td>\n",
" <td>0.066486</td>\n",
" <td>0.066486</td>\n",
" <td>NaN</td>\n",
" <td>-0.081891</td>\n",
" <td>-0.081891</td>\n",
" <td>-0.081891</td>\n",
" <td>-0.081891</td>\n",
" <td>NaN</td>\n",
" <td>-0.126787</td>\n",
" <td>-0.126787</td>\n",
" <td>-0.126787</td>\n",
" <td>-0.126787</td>\n",
" <td>NaN</td>\n",
" <td>-0.270175</td>\n",
" <td>-0.270175</td>\n",
" <td>-0.270175</td>\n",
" <td>-0.270175</td>\n",
" <td>NaN</td>\n",
" <td>0.082663</td>\n",
" <td>0.082663</td>\n",
" <td>0.082663</td>\n",
" <td>0.082663</td>\n",
" <td>NaN</td>\n",
" <td>-0.027607</td>\n",
" <td>-0.027607</td>\n",
" <td>-0.027607</td>\n",
" <td>-0.027607</td>\n",
" <td>NaN</td>\n",
" <td>0.071808</td>\n",
" <td>0.071808</td>\n",
" <td>0.071808</td>\n",
" <td>0.071808</td>\n",
" <td>NaN</td>\n",
" <td>0.284436</td>\n",
" <td>0.284436</td>\n",
" <td>0.284436</td>\n",
" <td>0.284436</td>\n",
" <td>NaN</td>\n",
" <td>-0.000577</td>\n",
" <td>-0.000577</td>\n",
" <td>-0.000577</td>\n",
" <td>-0.000577</td>\n",
" <td>NaN</td>\n",
" <td>-0.207844</td>\n",
" <td>0.045025</td>\n",
" <td>-0.005338</td>\n",
" <td>0.145838</td>\n",
" <td>0.211525</td>\n",
" <td>-0.142736</td>\n",
" <td>-0.211201</td>\n",
" <td>0.048844</td>\n",
" <td>-0.002806</td>\n",
" <td>0.149432</td>\n",
" <td>0.217863</td>\n",
" <td>-0.164226</td>\n",
" <td>-0.014702</td>\n",
" <td>0.170446</td>\n",
" <td>0.154358</td>\n",
" <td>0.268190</td>\n",
" <td>0.365030</td>\n",
" <td>0.103800</td>\n",
" <td>-0.374781</td>\n",
" <td>-0.122936</td>\n",
" <td>-0.202084</td>\n",
" <td>-0.029763</td>\n",
" <td>-0.015819</td>\n",
" <td>-0.285522</td>\n",
" <td>-0.460225</td>\n",
" <td>-0.453095</td>\n",
" <td>-0.521195</td>\n",
" <td>-0.404924</td>\n",
" <td>-0.486424</td>\n",
" <td>-0.232002</td>\n",
" <td>-0.432704</td>\n",
" <td>-0.459113</td>\n",
" <td>-0.561299</td>\n",
" <td>-0.430631</td>\n",
" <td>-0.492607</td>\n",
" <td>-0.229166</td>\n",
" <td>-0.149620</td>\n",
" <td>-0.140198</td>\n",
" <td>-0.026319</td>\n",
" <td>-0.086137</td>\n",
" <td>0.052805</td>\n",
" <td>0.023384</td>\n",
" <td>0.052805</td>\n",
" <td>0.117203</td>\n",
" <td>0.136393</td>\n",
" <td>0.105210</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 1 DISEASE GROUPING 2 \\\n",
"AGE_PERCENTIL_70th 0.402675 0.081684 -0.052838 \n",
"AGE_PERCENTIL_80th 0.372051 0.061864 -0.048820 \n",
"AGE_PERCENTIL_90th 0.322705 0.050118 0.134898 \n",
"AGE_PERCENTIL_Above 90th 0.305029 0.303608 0.053050 \n",
"ICU 0.240516 0.047593 0.116123 \n",
"\n",
" DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"AGE_PERCENTIL_70th 0.138639 0.189637 \n",
"AGE_PERCENTIL_80th -0.052347 0.039624 \n",
"AGE_PERCENTIL_90th 0.062662 -0.039135 \n",
"AGE_PERCENTIL_Above 90th 0.074493 -0.036992 \n",
"ICU 0.128431 0.143218 \n",
"\n",
" DISEASE GROUPING 5 DISEASE GROUPING 6 HTN \\\n",
"AGE_PERCENTIL_70th 0.053387 0.029902 0.116127 \n",
"AGE_PERCENTIL_80th 0.111784 -0.069903 0.081773 \n",
"AGE_PERCENTIL_90th 0.113210 -0.060631 0.093065 \n",
"AGE_PERCENTIL_Above 90th 0.262335 0.009324 0.148417 \n",
"ICU 0.107020 -0.000569 0.152904 \n",
"\n",
" IMMUNOCOMPROMISED OTHER ALBUMIN_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.154684 0.049643 0.033136 \n",
"AGE_PERCENTIL_80th -0.008411 -0.031463 0.001602 \n",
"AGE_PERCENTIL_90th 0.099587 -0.006109 0.013411 \n",
"AGE_PERCENTIL_Above 90th 0.078771 0.124363 -0.156210 \n",
"ICU 0.092280 -0.016866 -0.051499 \n",
"\n",
" ALBUMIN_MEAN ALBUMIN_MIN ALBUMIN_MAX \\\n",
"AGE_PERCENTIL_70th 0.033136 0.033136 0.033136 \n",
"AGE_PERCENTIL_80th 0.001602 0.001602 0.001602 \n",
"AGE_PERCENTIL_90th 0.013411 0.013411 0.013411 \n",
"AGE_PERCENTIL_Above 90th -0.156210 -0.156210 -0.156210 \n",
"ICU -0.051499 -0.051499 -0.051499 \n",
"\n",
" ALBUMIN_DIFF BE_ARTERIAL_MEDIAN BE_ARTERIAL_MEAN \\\n",
"AGE_PERCENTIL_70th NaN -0.002242 -0.002242 \n",
"AGE_PERCENTIL_80th NaN -0.033199 -0.033199 \n",
"AGE_PERCENTIL_90th NaN 0.008328 0.008328 \n",
"AGE_PERCENTIL_Above 90th NaN -0.027218 -0.027218 \n",
"ICU NaN 0.090883 0.090883 \n",
"\n",
" BE_ARTERIAL_MIN BE_ARTERIAL_MAX BE_ARTERIAL_DIFF \\\n",
"AGE_PERCENTIL_70th -0.002242 -0.002242 NaN \n",
"AGE_PERCENTIL_80th -0.033199 -0.033199 NaN \n",
"AGE_PERCENTIL_90th 0.008328 0.008328 NaN \n",
"AGE_PERCENTIL_Above 90th -0.027218 -0.027218 NaN \n",
"ICU 0.090883 0.090883 NaN \n",
"\n",
" BE_VENOUS_MEDIAN BE_VENOUS_MEAN BE_VENOUS_MIN \\\n",
"AGE_PERCENTIL_70th 0.055450 0.055450 0.055450 \n",
"AGE_PERCENTIL_80th 0.036012 0.036012 0.036012 \n",
"AGE_PERCENTIL_90th 0.001137 0.001137 0.001137 \n",
"AGE_PERCENTIL_Above 90th 0.068960 0.068960 0.068960 \n",
"ICU 0.071562 0.071562 0.071562 \n",
"\n",
" BE_VENOUS_MAX BE_VENOUS_DIFF BIC_ARTERIAL_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.055450 NaN -0.013313 \n",
"AGE_PERCENTIL_80th 0.036012 NaN -0.012301 \n",
"AGE_PERCENTIL_90th 0.001137 NaN -0.135710 \n",
"AGE_PERCENTIL_Above 90th 0.068960 NaN -0.010085 \n",
"ICU 0.071562 NaN 0.069185 \n",
"\n",
" BIC_ARTERIAL_MEAN BIC_ARTERIAL_MIN \\\n",
"AGE_PERCENTIL_70th -0.013313 -0.013313 \n",
"AGE_PERCENTIL_80th -0.012301 -0.012301 \n",
"AGE_PERCENTIL_90th -0.135710 -0.135710 \n",
"AGE_PERCENTIL_Above 90th -0.010085 -0.010085 \n",
"ICU 0.069185 0.069185 \n",
"\n",
" BIC_ARTERIAL_MAX BIC_ARTERIAL_DIFF \\\n",
"AGE_PERCENTIL_70th -0.013313 NaN \n",
"AGE_PERCENTIL_80th -0.012301 NaN \n",
"AGE_PERCENTIL_90th -0.135710 NaN \n",
"AGE_PERCENTIL_Above 90th -0.010085 NaN \n",
"ICU 0.069185 NaN \n",
"\n",
" BIC_VENOUS_MEDIAN BIC_VENOUS_MEAN BIC_VENOUS_MIN \\\n",
"AGE_PERCENTIL_70th 0.042387 0.042387 0.042387 \n",
"AGE_PERCENTIL_80th 0.031392 0.031392 0.031392 \n",
"AGE_PERCENTIL_90th -0.023979 -0.023979 -0.023979 \n",
"AGE_PERCENTIL_Above 90th -0.026674 -0.026674 -0.026674 \n",
"ICU -0.128091 -0.128091 -0.128091 \n",
"\n",
" BIC_VENOUS_MAX BIC_VENOUS_DIFF BILLIRUBIN_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.042387 NaN 0.043088 \n",
"AGE_PERCENTIL_80th 0.031392 NaN 0.023247 \n",
"AGE_PERCENTIL_90th -0.023979 NaN 0.012834 \n",
"AGE_PERCENTIL_Above 90th -0.026674 NaN -0.013805 \n",
"ICU -0.128091 NaN 0.106479 \n",
"\n",
" BILLIRUBIN_MEAN BILLIRUBIN_MIN BILLIRUBIN_MAX \\\n",
"AGE_PERCENTIL_70th 0.043088 0.043088 0.043088 \n",
"AGE_PERCENTIL_80th 0.023247 0.023247 0.023247 \n",
"AGE_PERCENTIL_90th 0.012834 0.012834 0.012834 \n",
"AGE_PERCENTIL_Above 90th -0.013805 -0.013805 -0.013805 \n",
"ICU 0.106479 0.106479 0.106479 \n",
"\n",
" BILLIRUBIN_DIFF BLAST_MEDIAN BLAST_MEAN \\\n",
"AGE_PERCENTIL_70th NaN -0.022161 -0.022161 \n",
"AGE_PERCENTIL_80th NaN -0.020476 -0.020476 \n",
"AGE_PERCENTIL_90th NaN 0.212447 0.212447 \n",
"AGE_PERCENTIL_Above 90th NaN -0.016787 -0.016787 \n",
"ICU NaN 0.087800 0.087800 \n",
"\n",
" BLAST_MIN BLAST_MAX BLAST_DIFF CALCIUM_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.022161 -0.022161 NaN -0.033941 \n",
"AGE_PERCENTIL_80th -0.020476 -0.020476 NaN -0.106084 \n",
"AGE_PERCENTIL_90th 0.212447 0.212447 NaN -0.008451 \n",
"AGE_PERCENTIL_Above 90th -0.016787 -0.016787 NaN -0.008727 \n",
"ICU 0.087800 0.087800 NaN -0.281260 \n",
"\n",
" CALCIUM_MEAN CALCIUM_MIN CALCIUM_MAX \\\n",
"AGE_PERCENTIL_70th -0.033941 -0.033941 -0.033941 \n",
"AGE_PERCENTIL_80th -0.106084 -0.106084 -0.106084 \n",
"AGE_PERCENTIL_90th -0.008451 -0.008451 -0.008451 \n",
"AGE_PERCENTIL_Above 90th -0.008727 -0.008727 -0.008727 \n",
"ICU -0.281260 -0.281260 -0.281260 \n",
"\n",
" CALCIUM_DIFF CREATININ_MEDIAN CREATININ_MEAN \\\n",
"AGE_PERCENTIL_70th NaN 0.012805 0.012805 \n",
"AGE_PERCENTIL_80th NaN 0.187437 0.187437 \n",
"AGE_PERCENTIL_90th NaN 0.011321 0.011321 \n",
"AGE_PERCENTIL_Above 90th NaN 0.048913 0.048913 \n",
"ICU NaN 0.219298 0.219298 \n",
"\n",
" CREATININ_MIN CREATININ_MAX CREATININ_DIFF \\\n",
"AGE_PERCENTIL_70th 0.012805 0.012805 NaN \n",
"AGE_PERCENTIL_80th 0.187437 0.187437 NaN \n",
"AGE_PERCENTIL_90th 0.011321 0.011321 NaN \n",
"AGE_PERCENTIL_Above 90th 0.048913 0.048913 NaN \n",
"ICU 0.219298 0.219298 NaN \n",
"\n",
" FFA_MEDIAN FFA_MEAN FFA_MIN FFA_MAX FFA_DIFF \\\n",
"AGE_PERCENTIL_70th -0.019508 -0.019508 -0.019508 -0.019508 NaN \n",
"AGE_PERCENTIL_80th 0.028170 0.028170 0.028170 0.028170 NaN \n",
"AGE_PERCENTIL_90th 0.018527 0.018527 0.018527 0.018527 NaN \n",
"AGE_PERCENTIL_Above 90th -0.051167 -0.051167 -0.051167 -0.051167 NaN \n",
"ICU 0.101017 0.101017 0.101017 0.101017 NaN \n",
"\n",
" GGT_MEDIAN GGT_MEAN GGT_MIN GGT_MAX GGT_DIFF \\\n",
"AGE_PERCENTIL_70th -0.009802 -0.009802 -0.009802 -0.009802 NaN \n",
"AGE_PERCENTIL_80th -0.030858 -0.030858 -0.030858 -0.030858 NaN \n",
"AGE_PERCENTIL_90th -0.041682 -0.041682 -0.041682 -0.041682 NaN \n",
"AGE_PERCENTIL_Above 90th -0.064201 -0.064201 -0.064201 -0.064201 NaN \n",
"ICU 0.075144 0.075144 0.075144 0.075144 NaN \n",
"\n",
" GLUCOSE_MEDIAN GLUCOSE_MEAN GLUCOSE_MIN \\\n",
"AGE_PERCENTIL_70th -0.007641 -0.007641 -0.007641 \n",
"AGE_PERCENTIL_80th -0.006491 -0.006491 -0.006491 \n",
"AGE_PERCENTIL_90th -0.046688 -0.046688 -0.046688 \n",
"AGE_PERCENTIL_Above 90th 0.182894 0.182894 0.182894 \n",
"ICU 0.186633 0.186633 0.186633 \n",
"\n",
" GLUCOSE_MAX GLUCOSE_DIFF HEMATOCRITE_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.007641 NaN -0.189385 \n",
"AGE_PERCENTIL_80th -0.006491 NaN -0.050288 \n",
"AGE_PERCENTIL_90th -0.046688 NaN -0.075065 \n",
"AGE_PERCENTIL_Above 90th 0.182894 NaN -0.168841 \n",
"ICU 0.186633 NaN -0.064099 \n",
"\n",
" HEMATOCRITE_MEAN HEMATOCRITE_MIN HEMATOCRITE_MAX \\\n",
"AGE_PERCENTIL_70th -0.189385 -0.189385 -0.189385 \n",
"AGE_PERCENTIL_80th -0.050288 -0.050288 -0.050288 \n",
"AGE_PERCENTIL_90th -0.075065 -0.075065 -0.075065 \n",
"AGE_PERCENTIL_Above 90th -0.168841 -0.168841 -0.168841 \n",
"ICU -0.064099 -0.064099 -0.064099 \n",
"\n",
" HEMATOCRITE_DIFF HEMOGLOBIN_MEDIAN \\\n",
"AGE_PERCENTIL_70th NaN -0.181559 \n",
"AGE_PERCENTIL_80th NaN -0.044214 \n",
"AGE_PERCENTIL_90th NaN -0.092330 \n",
"AGE_PERCENTIL_Above 90th NaN -0.184098 \n",
"ICU NaN -0.069985 \n",
"\n",
" HEMOGLOBIN_MEAN HEMOGLOBIN_MIN HEMOGLOBIN_MAX \\\n",
"AGE_PERCENTIL_70th -0.181559 -0.181559 -0.181559 \n",
"AGE_PERCENTIL_80th -0.044214 -0.044214 -0.044214 \n",
"AGE_PERCENTIL_90th -0.092330 -0.092330 -0.092330 \n",
"AGE_PERCENTIL_Above 90th -0.184098 -0.184098 -0.184098 \n",
"ICU -0.069985 -0.069985 -0.069985 \n",
"\n",
" HEMOGLOBIN_DIFF INR_MEDIAN INR_MEAN INR_MIN \\\n",
"AGE_PERCENTIL_70th NaN 0.121959 0.121959 0.121959 \n",
"AGE_PERCENTIL_80th NaN 0.066156 0.066156 0.066156 \n",
"AGE_PERCENTIL_90th NaN 0.003333 0.003333 0.003333 \n",
"AGE_PERCENTIL_Above 90th NaN -0.019227 -0.019227 -0.019227 \n",
"ICU NaN 0.125135 0.125135 0.125135 \n",
"\n",
" INR_MAX INR_DIFF LACTATE_MEDIAN LACTATE_MEAN \\\n",
"AGE_PERCENTIL_70th 0.121959 NaN 0.080602 0.080602 \n",
"AGE_PERCENTIL_80th 0.066156 NaN 0.072293 0.072293 \n",
"AGE_PERCENTIL_90th 0.003333 NaN -0.157023 -0.157023 \n",
"AGE_PERCENTIL_Above 90th -0.019227 NaN 0.002241 0.002241 \n",
"ICU 0.125135 NaN -0.206652 -0.206652 \n",
"\n",
" LACTATE_MIN LACTATE_MAX LACTATE_DIFF \\\n",
"AGE_PERCENTIL_70th 0.080602 0.080602 NaN \n",
"AGE_PERCENTIL_80th 0.072293 0.072293 NaN \n",
"AGE_PERCENTIL_90th -0.157023 -0.157023 NaN \n",
"AGE_PERCENTIL_Above 90th 0.002241 0.002241 NaN \n",
"ICU -0.206652 -0.206652 NaN \n",
"\n",
" LEUKOCYTES_MEDIAN LEUKOCYTES_MEAN LEUKOCYTES_MIN \\\n",
"AGE_PERCENTIL_70th -0.035692 -0.035692 -0.035692 \n",
"AGE_PERCENTIL_80th -0.045179 -0.045179 -0.045179 \n",
"AGE_PERCENTIL_90th 0.067807 0.067807 0.067807 \n",
"AGE_PERCENTIL_Above 90th 0.059318 0.059318 0.059318 \n",
"ICU 0.116605 0.116605 0.116605 \n",
"\n",
" LEUKOCYTES_MAX LEUKOCYTES_DIFF LINFOCITOS_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.035692 NaN -0.019683 \n",
"AGE_PERCENTIL_80th -0.045179 NaN -0.124717 \n",
"AGE_PERCENTIL_90th 0.067807 NaN -0.024487 \n",
"AGE_PERCENTIL_Above 90th 0.059318 NaN -0.062136 \n",
"ICU 0.116605 NaN -0.257798 \n",
"\n",
" LINFOCITOS_MEAN LINFOCITOS_MIN LINFOCITOS_MAX \\\n",
"AGE_PERCENTIL_70th -0.019683 -0.019683 -0.019683 \n",
"AGE_PERCENTIL_80th -0.124717 -0.124717 -0.124717 \n",
"AGE_PERCENTIL_90th -0.024487 -0.024487 -0.024487 \n",
"AGE_PERCENTIL_Above 90th -0.062136 -0.062136 -0.062136 \n",
"ICU -0.257798 -0.257798 -0.257798 \n",
"\n",
" LINFOCITOS_DIFF NEUTROPHILES_MEDIAN \\\n",
"AGE_PERCENTIL_70th NaN -0.031877 \n",
"AGE_PERCENTIL_80th NaN -0.017195 \n",
"AGE_PERCENTIL_90th NaN 0.068433 \n",
"AGE_PERCENTIL_Above 90th NaN 0.062926 \n",
"ICU NaN 0.196802 \n",
"\n",
" NEUTROPHILES_MEAN NEUTROPHILES_MIN \\\n",
"AGE_PERCENTIL_70th -0.031877 -0.031877 \n",
"AGE_PERCENTIL_80th -0.017195 -0.017195 \n",
"AGE_PERCENTIL_90th 0.068433 0.068433 \n",
"AGE_PERCENTIL_Above 90th 0.062926 0.062926 \n",
"ICU 0.196802 0.196802 \n",
"\n",
" NEUTROPHILES_MAX NEUTROPHILES_DIFF \\\n",
"AGE_PERCENTIL_70th -0.031877 NaN \n",
"AGE_PERCENTIL_80th -0.017195 NaN \n",
"AGE_PERCENTIL_90th 0.068433 NaN \n",
"AGE_PERCENTIL_Above 90th 0.062926 NaN \n",
"ICU 0.196802 NaN \n",
"\n",
" P02_ARTERIAL_MEDIAN P02_ARTERIAL_MEAN \\\n",
"AGE_PERCENTIL_70th -0.046734 -0.046734 \n",
"AGE_PERCENTIL_80th 0.016702 0.016702 \n",
"AGE_PERCENTIL_90th 0.006427 0.006427 \n",
"AGE_PERCENTIL_Above 90th 0.013694 0.013694 \n",
"ICU -0.034177 -0.034177 \n",
"\n",
" P02_ARTERIAL_MIN P02_ARTERIAL_MAX \\\n",
"AGE_PERCENTIL_70th -0.046734 -0.046734 \n",
"AGE_PERCENTIL_80th 0.016702 0.016702 \n",
"AGE_PERCENTIL_90th 0.006427 0.006427 \n",
"AGE_PERCENTIL_Above 90th 0.013694 0.013694 \n",
"ICU -0.034177 -0.034177 \n",
"\n",
" P02_ARTERIAL_DIFF P02_VENOUS_MEDIAN \\\n",
"AGE_PERCENTIL_70th NaN 0.035907 \n",
"AGE_PERCENTIL_80th NaN 0.008631 \n",
"AGE_PERCENTIL_90th NaN -0.026206 \n",
"AGE_PERCENTIL_Above 90th NaN 0.107393 \n",
"ICU NaN -0.033937 \n",
"\n",
" P02_VENOUS_MEAN P02_VENOUS_MIN P02_VENOUS_MAX \\\n",
"AGE_PERCENTIL_70th 0.035907 0.035907 0.035907 \n",
"AGE_PERCENTIL_80th 0.008631 0.008631 0.008631 \n",
"AGE_PERCENTIL_90th -0.026206 -0.026206 -0.026206 \n",
"AGE_PERCENTIL_Above 90th 0.107393 0.107393 0.107393 \n",
"ICU -0.033937 -0.033937 -0.033937 \n",
"\n",
" P02_VENOUS_DIFF PC02_ARTERIAL_MEDIAN \\\n",
"AGE_PERCENTIL_70th NaN -0.068907 \n",
"AGE_PERCENTIL_80th NaN 0.037350 \n",
"AGE_PERCENTIL_90th NaN -0.157444 \n",
"AGE_PERCENTIL_Above 90th NaN 0.030621 \n",
"ICU NaN -0.025753 \n",
"\n",
" PC02_ARTERIAL_MEAN PC02_ARTERIAL_MIN \\\n",
"AGE_PERCENTIL_70th -0.068907 -0.068907 \n",
"AGE_PERCENTIL_80th 0.037350 0.037350 \n",
"AGE_PERCENTIL_90th -0.157444 -0.157444 \n",
"AGE_PERCENTIL_Above 90th 0.030621 0.030621 \n",
"ICU -0.025753 -0.025753 \n",
"\n",
" PC02_ARTERIAL_MAX PC02_ARTERIAL_DIFF \\\n",
"AGE_PERCENTIL_70th -0.068907 NaN \n",
"AGE_PERCENTIL_80th 0.037350 NaN \n",
"AGE_PERCENTIL_90th -0.157444 NaN \n",
"AGE_PERCENTIL_Above 90th 0.030621 NaN \n",
"ICU -0.025753 NaN \n",
"\n",
" PC02_VENOUS_MEDIAN PC02_VENOUS_MEAN \\\n",
"AGE_PERCENTIL_70th -0.034507 -0.034507 \n",
"AGE_PERCENTIL_80th -0.038268 -0.038268 \n",
"AGE_PERCENTIL_90th -0.038205 -0.038205 \n",
"AGE_PERCENTIL_Above 90th 0.035268 0.035268 \n",
"ICU -0.151272 -0.151272 \n",
"\n",
" PC02_VENOUS_MIN PC02_VENOUS_MAX PC02_VENOUS_DIFF \\\n",
"AGE_PERCENTIL_70th -0.034507 -0.034507 NaN \n",
"AGE_PERCENTIL_80th -0.038268 -0.038268 NaN \n",
"AGE_PERCENTIL_90th -0.038205 -0.038205 NaN \n",
"AGE_PERCENTIL_Above 90th 0.035268 0.035268 NaN \n",
"ICU -0.151272 -0.151272 NaN \n",
"\n",
" PCR_MEDIAN PCR_MEAN PCR_MIN PCR_MAX PCR_DIFF \\\n",
"AGE_PERCENTIL_70th 0.062130 0.062130 0.062130 0.062130 NaN \n",
"AGE_PERCENTIL_80th -0.008421 -0.008421 -0.008421 -0.008421 NaN \n",
"AGE_PERCENTIL_90th 0.011468 0.011468 0.011468 0.011468 NaN \n",
"AGE_PERCENTIL_Above 90th -0.058987 -0.058987 -0.058987 -0.058987 NaN \n",
"ICU 0.342681 0.342681 0.342681 0.342681 NaN \n",
"\n",
" PH_ARTERIAL_MEDIAN PH_ARTERIAL_MEAN \\\n",
"AGE_PERCENTIL_70th 0.049093 0.049093 \n",
"AGE_PERCENTIL_80th -0.035351 -0.035351 \n",
"AGE_PERCENTIL_90th 0.051011 0.051011 \n",
"AGE_PERCENTIL_Above 90th -0.028983 -0.028983 \n",
"ICU 0.044200 0.044200 \n",
"\n",
" PH_ARTERIAL_MIN PH_ARTERIAL_MAX PH_ARTERIAL_DIFF \\\n",
"AGE_PERCENTIL_70th 0.049093 0.049093 NaN \n",
"AGE_PERCENTIL_80th -0.035351 -0.035351 NaN \n",
"AGE_PERCENTIL_90th 0.051011 0.051011 NaN \n",
"AGE_PERCENTIL_Above 90th -0.028983 -0.028983 NaN \n",
"ICU 0.044200 0.044200 NaN \n",
"\n",
" PH_VENOUS_MEDIAN PH_VENOUS_MEAN PH_VENOUS_MIN \\\n",
"AGE_PERCENTIL_70th 0.084124 0.084124 0.084124 \n",
"AGE_PERCENTIL_80th 0.097817 0.097817 0.097817 \n",
"AGE_PERCENTIL_90th 0.033351 0.033351 0.033351 \n",
"AGE_PERCENTIL_Above 90th -0.098350 -0.098350 -0.098350 \n",
"ICU 0.052446 0.052446 0.052446 \n",
"\n",
" PH_VENOUS_MAX PH_VENOUS_DIFF PLATELETS_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.084124 NaN -0.092542 \n",
"AGE_PERCENTIL_80th 0.097817 NaN -0.176491 \n",
"AGE_PERCENTIL_90th 0.033351 NaN -0.031250 \n",
"AGE_PERCENTIL_Above 90th -0.098350 NaN -0.028189 \n",
"ICU 0.052446 NaN -0.205424 \n",
"\n",
" PLATELETS_MEAN PLATELETS_MIN PLATELETS_MAX \\\n",
"AGE_PERCENTIL_70th -0.092542 -0.092542 -0.092542 \n",
"AGE_PERCENTIL_80th -0.176491 -0.176491 -0.176491 \n",
"AGE_PERCENTIL_90th -0.031250 -0.031250 -0.031250 \n",
"AGE_PERCENTIL_Above 90th -0.028189 -0.028189 -0.028189 \n",
"ICU -0.205424 -0.205424 -0.205424 \n",
"\n",
" PLATELETS_DIFF POTASSIUM_MEDIAN POTASSIUM_MEAN \\\n",
"AGE_PERCENTIL_70th NaN -0.030214 -0.030214 \n",
"AGE_PERCENTIL_80th NaN -0.119943 -0.119943 \n",
"AGE_PERCENTIL_90th NaN 0.134179 0.134179 \n",
"AGE_PERCENTIL_Above 90th NaN 0.138645 0.138645 \n",
"ICU NaN 0.066486 0.066486 \n",
"\n",
" POTASSIUM_MIN POTASSIUM_MAX POTASSIUM_DIFF \\\n",
"AGE_PERCENTIL_70th -0.030214 -0.030214 NaN \n",
"AGE_PERCENTIL_80th -0.119943 -0.119943 NaN \n",
"AGE_PERCENTIL_90th 0.134179 0.134179 NaN \n",
"AGE_PERCENTIL_Above 90th 0.138645 0.138645 NaN \n",
"ICU 0.066486 0.066486 NaN \n",
"\n",
" SAT02_ARTERIAL_MEDIAN SAT02_ARTERIAL_MEAN \\\n",
"AGE_PERCENTIL_70th 0.001932 0.001932 \n",
"AGE_PERCENTIL_80th 0.021159 0.021159 \n",
"AGE_PERCENTIL_90th 0.012351 0.012351 \n",
"AGE_PERCENTIL_Above 90th 0.017347 0.017347 \n",
"ICU -0.081891 -0.081891 \n",
"\n",
" SAT02_ARTERIAL_MIN SAT02_ARTERIAL_MAX \\\n",
"AGE_PERCENTIL_70th 0.001932 0.001932 \n",
"AGE_PERCENTIL_80th 0.021159 0.021159 \n",
"AGE_PERCENTIL_90th 0.012351 0.012351 \n",
"AGE_PERCENTIL_Above 90th 0.017347 0.017347 \n",
"ICU -0.081891 -0.081891 \n",
"\n",
" SAT02_ARTERIAL_DIFF SAT02_VENOUS_MEDIAN \\\n",
"AGE_PERCENTIL_70th NaN -0.007141 \n",
"AGE_PERCENTIL_80th NaN 0.062060 \n",
"AGE_PERCENTIL_90th NaN -0.093800 \n",
"AGE_PERCENTIL_Above 90th NaN -0.035564 \n",
"ICU NaN -0.126787 \n",
"\n",
" SAT02_VENOUS_MEAN SAT02_VENOUS_MIN \\\n",
"AGE_PERCENTIL_70th -0.007141 -0.007141 \n",
"AGE_PERCENTIL_80th 0.062060 0.062060 \n",
"AGE_PERCENTIL_90th -0.093800 -0.093800 \n",
"AGE_PERCENTIL_Above 90th -0.035564 -0.035564 \n",
"ICU -0.126787 -0.126787 \n",
"\n",
" SAT02_VENOUS_MAX SAT02_VENOUS_DIFF SODIUM_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.007141 NaN -0.037360 \n",
"AGE_PERCENTIL_80th 0.062060 NaN -0.004862 \n",
"AGE_PERCENTIL_90th -0.093800 NaN -0.052278 \n",
"AGE_PERCENTIL_Above 90th -0.035564 NaN -0.078044 \n",
"ICU -0.126787 NaN -0.270175 \n",
"\n",
" SODIUM_MEAN SODIUM_MIN SODIUM_MAX SODIUM_DIFF \\\n",
"AGE_PERCENTIL_70th -0.037360 -0.037360 -0.037360 NaN \n",
"AGE_PERCENTIL_80th -0.004862 -0.004862 -0.004862 NaN \n",
"AGE_PERCENTIL_90th -0.052278 -0.052278 -0.052278 NaN \n",
"AGE_PERCENTIL_Above 90th -0.078044 -0.078044 -0.078044 NaN \n",
"ICU -0.270175 -0.270175 -0.270175 NaN \n",
"\n",
" TGO_MEDIAN TGO_MEAN TGO_MIN TGO_MAX TGO_DIFF \\\n",
"AGE_PERCENTIL_70th 0.010614 0.010614 0.010614 0.010614 NaN \n",
"AGE_PERCENTIL_80th 0.166311 0.166311 0.166311 0.166311 NaN \n",
"AGE_PERCENTIL_90th -0.032935 -0.032935 -0.032935 -0.032935 NaN \n",
"AGE_PERCENTIL_Above 90th -0.035721 -0.035721 -0.035721 -0.035721 NaN \n",
"ICU 0.082663 0.082663 0.082663 0.082663 NaN \n",
"\n",
" TGP_MEDIAN TGP_MEAN TGP_MIN TGP_MAX TGP_DIFF \\\n",
"AGE_PERCENTIL_70th 0.018319 0.018319 0.018319 0.018319 NaN \n",
"AGE_PERCENTIL_80th 0.073675 0.073675 0.073675 0.073675 NaN \n",
"AGE_PERCENTIL_90th -0.045916 -0.045916 -0.045916 -0.045916 NaN \n",
"AGE_PERCENTIL_Above 90th -0.103004 -0.103004 -0.103004 -0.103004 NaN \n",
"ICU -0.027607 -0.027607 -0.027607 -0.027607 NaN \n",
"\n",
" TTPA_MEDIAN TTPA_MEAN TTPA_MIN TTPA_MAX \\\n",
"AGE_PERCENTIL_70th 0.066917 0.066917 0.066917 0.066917 \n",
"AGE_PERCENTIL_80th -0.090077 -0.090077 -0.090077 -0.090077 \n",
"AGE_PERCENTIL_90th 0.052318 0.052318 0.052318 0.052318 \n",
"AGE_PERCENTIL_Above 90th -0.067098 -0.067098 -0.067098 -0.067098 \n",
"ICU 0.071808 0.071808 0.071808 0.071808 \n",
"\n",
" TTPA_DIFF UREA_MEDIAN UREA_MEAN UREA_MIN \\\n",
"AGE_PERCENTIL_70th NaN 0.080082 0.080082 0.080082 \n",
"AGE_PERCENTIL_80th NaN 0.246949 0.246949 0.246949 \n",
"AGE_PERCENTIL_90th NaN 0.065191 0.065191 0.065191 \n",
"AGE_PERCENTIL_Above 90th NaN 0.236355 0.236355 0.236355 \n",
"ICU NaN 0.284436 0.284436 0.284436 \n",
"\n",
" UREA_MAX UREA_DIFF DIMER_MEDIAN DIMER_MEAN \\\n",
"AGE_PERCENTIL_70th 0.080082 NaN -0.028426 -0.028426 \n",
"AGE_PERCENTIL_80th 0.246949 NaN 0.066066 0.066066 \n",
"AGE_PERCENTIL_90th 0.065191 NaN -0.007696 -0.007696 \n",
"AGE_PERCENTIL_Above 90th 0.236355 NaN 0.075485 0.075485 \n",
"ICU 0.284436 NaN -0.000577 -0.000577 \n",
"\n",
" DIMER_MIN DIMER_MAX DIMER_DIFF \\\n",
"AGE_PERCENTIL_70th -0.028426 -0.028426 NaN \n",
"AGE_PERCENTIL_80th 0.066066 0.066066 NaN \n",
"AGE_PERCENTIL_90th -0.007696 -0.007696 NaN \n",
"AGE_PERCENTIL_Above 90th 0.075485 0.075485 NaN \n",
"ICU -0.000577 -0.000577 NaN \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN \\\n",
"AGE_PERCENTIL_70th -0.061789 \n",
"AGE_PERCENTIL_80th -0.010905 \n",
"AGE_PERCENTIL_90th 0.035194 \n",
"AGE_PERCENTIL_Above 90th -0.148409 \n",
"ICU -0.207844 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEAN HEART_RATE_MEAN \\\n",
"AGE_PERCENTIL_70th 0.084698 -0.131850 \n",
"AGE_PERCENTIL_80th 0.065313 -0.090179 \n",
"AGE_PERCENTIL_90th 0.213388 -0.042347 \n",
"AGE_PERCENTIL_Above 90th 0.055248 0.002933 \n",
"ICU 0.045025 -0.005338 \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN \\\n",
"AGE_PERCENTIL_70th 0.068438 -0.080325 \n",
"AGE_PERCENTIL_80th 0.008452 -0.009846 \n",
"AGE_PERCENTIL_90th 0.020020 0.006909 \n",
"AGE_PERCENTIL_Above 90th 0.152784 0.016918 \n",
"ICU 0.145838 0.211525 \n",
"\n",
" OXYGEN_SATURATION_MEAN \\\n",
"AGE_PERCENTIL_70th -0.072574 \n",
"AGE_PERCENTIL_80th -0.060254 \n",
"AGE_PERCENTIL_90th -0.033732 \n",
"AGE_PERCENTIL_Above 90th 0.008539 \n",
"ICU -0.142736 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.059360 \n",
"AGE_PERCENTIL_80th -0.011728 \n",
"AGE_PERCENTIL_90th 0.032509 \n",
"AGE_PERCENTIL_Above 90th -0.144490 \n",
"ICU -0.211201 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MEDIAN HEART_RATE_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.086414 -0.129918 \n",
"AGE_PERCENTIL_80th 0.073211 -0.063178 \n",
"AGE_PERCENTIL_90th 0.212713 -0.037758 \n",
"AGE_PERCENTIL_Above 90th 0.049881 0.003529 \n",
"ICU 0.048844 -0.002806 \n",
"\n",
" RESPIRATORY_RATE_MEDIAN TEMPERATURE_MEDIAN \\\n",
"AGE_PERCENTIL_70th 0.060472 -0.091464 \n",
"AGE_PERCENTIL_80th 0.012784 -0.009997 \n",
"AGE_PERCENTIL_90th 0.020266 0.017651 \n",
"AGE_PERCENTIL_Above 90th 0.156166 0.015435 \n",
"ICU 0.149432 0.217863 \n",
"\n",
" OXYGEN_SATURATION_MEDIAN \\\n",
"AGE_PERCENTIL_70th -0.040348 \n",
"AGE_PERCENTIL_80th -0.050306 \n",
"AGE_PERCENTIL_90th -0.036191 \n",
"AGE_PERCENTIL_Above 90th 0.009033 \n",
"ICU -0.164226 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MIN \\\n",
"AGE_PERCENTIL_70th -0.036504 \n",
"AGE_PERCENTIL_80th 0.016969 \n",
"AGE_PERCENTIL_90th 0.070790 \n",
"AGE_PERCENTIL_Above 90th -0.118053 \n",
"ICU -0.014702 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN \\\n",
"AGE_PERCENTIL_70th 0.085011 -0.117564 \n",
"AGE_PERCENTIL_80th 0.073879 -0.095074 \n",
"AGE_PERCENTIL_90th 0.227708 0.005969 \n",
"AGE_PERCENTIL_Above 90th 0.064086 0.024762 \n",
"ICU 0.170446 0.154358 \n",
"\n",
" RESPIRATORY_RATE_MIN TEMPERATURE_MIN \\\n",
"AGE_PERCENTIL_70th 0.048441 -0.070054 \n",
"AGE_PERCENTIL_80th 0.017877 0.015329 \n",
"AGE_PERCENTIL_90th 0.028998 0.042049 \n",
"AGE_PERCENTIL_Above 90th 0.170116 0.029084 \n",
"ICU 0.268190 0.365030 \n",
"\n",
" OXYGEN_SATURATION_MIN BLOODPRESSURE_DIASTOLIC_MAX \\\n",
"AGE_PERCENTIL_70th -0.038569 -0.035095 \n",
"AGE_PERCENTIL_80th 0.011042 -0.057432 \n",
"AGE_PERCENTIL_90th -0.013238 -0.006645 \n",
"AGE_PERCENTIL_Above 90th 0.045574 -0.140688 \n",
"ICU 0.103800 -0.374781 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MAX HEART_RATE_MAX \\\n",
"AGE_PERCENTIL_70th 0.077661 -0.138762 \n",
"AGE_PERCENTIL_80th 0.036275 -0.112635 \n",
"AGE_PERCENTIL_90th 0.173577 -0.087677 \n",
"AGE_PERCENTIL_Above 90th 0.060473 -0.016091 \n",
"ICU -0.122936 -0.202084 \n",
"\n",
" RESPIRATORY_RATE_MAX TEMPERATURE_MAX \\\n",
"AGE_PERCENTIL_70th 0.074757 -0.060933 \n",
"AGE_PERCENTIL_80th -0.016837 -0.033501 \n",
"AGE_PERCENTIL_90th -0.025425 -0.055713 \n",
"AGE_PERCENTIL_Above 90th 0.112055 0.006042 \n",
"ICU -0.029763 -0.015819 \n",
"\n",
" OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"AGE_PERCENTIL_70th -0.043424 -0.002392 \n",
"AGE_PERCENTIL_80th -0.093839 -0.092799 \n",
"AGE_PERCENTIL_90th -0.093220 -0.090489 \n",
"AGE_PERCENTIL_Above 90th -0.018779 -0.042366 \n",
"ICU -0.285522 -0.460225 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF \\\n",
"AGE_PERCENTIL_70th -0.009271 -0.033103 \n",
"AGE_PERCENTIL_80th -0.056737 -0.027386 \n",
"AGE_PERCENTIL_90th -0.078546 -0.137506 \n",
"AGE_PERCENTIL_Above 90th -0.003984 -0.059618 \n",
"ICU -0.453095 -0.521195 \n",
"\n",
" RESPIRATORY_RATE_DIFF TEMPERATURE_DIFF \\\n",
"AGE_PERCENTIL_70th 0.042641 0.012466 \n",
"AGE_PERCENTIL_80th -0.048452 -0.061946 \n",
"AGE_PERCENTIL_90th -0.075868 -0.124175 \n",
"AGE_PERCENTIL_Above 90th -0.067668 -0.029522 \n",
"ICU -0.404924 -0.486424 \n",
"\n",
" OXYGEN_SATURATION_DIFF \\\n",
"AGE_PERCENTIL_70th 0.019395 \n",
"AGE_PERCENTIL_80th -0.053039 \n",
"AGE_PERCENTIL_90th -0.028338 \n",
"AGE_PERCENTIL_Above 90th -0.054236 \n",
"ICU -0.232002 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n",
"AGE_PERCENTIL_70th -0.001520 \n",
"AGE_PERCENTIL_80th -0.090933 \n",
"AGE_PERCENTIL_90th -0.085449 \n",
"AGE_PERCENTIL_Above 90th -0.014615 \n",
"ICU -0.432704 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF_REL \\\n",
"AGE_PERCENTIL_70th -0.011538 \n",
"AGE_PERCENTIL_80th -0.070643 \n",
"AGE_PERCENTIL_90th -0.081142 \n",
"AGE_PERCENTIL_Above 90th -0.007365 \n",
"ICU -0.459113 \n",
"\n",
" HEART_RATE_DIFF_REL RESPIRATORY_RATE_DIFF_REL \\\n",
"AGE_PERCENTIL_70th -0.018430 0.049075 \n",
"AGE_PERCENTIL_80th -0.063098 -0.054256 \n",
"AGE_PERCENTIL_90th -0.147246 -0.075906 \n",
"AGE_PERCENTIL_Above 90th -0.058710 -0.071135 \n",
"ICU -0.561299 -0.430631 \n",
"\n",
" TEMPERATURE_DIFF_REL OXYGEN_SATURATION_DIFF_REL \\\n",
"AGE_PERCENTIL_70th 0.016294 0.022468 \n",
"AGE_PERCENTIL_80th -0.062087 -0.053039 \n",
"AGE_PERCENTIL_90th -0.125159 -0.029484 \n",
"AGE_PERCENTIL_Above 90th -0.029038 -0.053933 \n",
"ICU -0.492607 -0.229166 \n",
"\n",
" AGE_PERCENTIL_10th AGE_PERCENTIL_20th \\\n",
"AGE_PERCENTIL_70th -0.126406 -0.130378 \n",
"AGE_PERCENTIL_80th -0.116793 -0.120463 \n",
"AGE_PERCENTIL_90th -0.101302 -0.104485 \n",
"AGE_PERCENTIL_Above 90th -0.095753 -0.098762 \n",
"ICU -0.149620 -0.140198 \n",
"\n",
" AGE_PERCENTIL_30th AGE_PERCENTIL_40th \\\n",
"AGE_PERCENTIL_70th -0.122369 -0.120324 \n",
"AGE_PERCENTIL_80th -0.113063 -0.111174 \n",
"AGE_PERCENTIL_90th -0.098067 -0.096428 \n",
"AGE_PERCENTIL_Above 90th -0.092696 -0.091147 \n",
"ICU -0.026319 -0.086137 \n",
"\n",
" AGE_PERCENTIL_50th AGE_PERCENTIL_60th \\\n",
"AGE_PERCENTIL_70th -0.114068 -0.109784 \n",
"AGE_PERCENTIL_80th -0.105393 -0.101435 \n",
"AGE_PERCENTIL_90th -0.091415 -0.087981 \n",
"AGE_PERCENTIL_Above 90th -0.086408 -0.083162 \n",
"ICU 0.052805 0.023384 \n",
"\n",
" AGE_PERCENTIL_70th AGE_PERCENTIL_80th \\\n",
"AGE_PERCENTIL_70th 1.000000 -0.105393 \n",
"AGE_PERCENTIL_80th -0.105393 1.000000 \n",
"AGE_PERCENTIL_90th -0.091415 -0.084463 \n",
"AGE_PERCENTIL_Above 90th -0.086408 -0.079836 \n",
"ICU 0.052805 0.117203 \n",
"\n",
" AGE_PERCENTIL_90th AGE_PERCENTIL_Above 90th \\\n",
"AGE_PERCENTIL_70th -0.091415 -0.086408 \n",
"AGE_PERCENTIL_80th -0.084463 -0.079836 \n",
"AGE_PERCENTIL_90th 1.000000 -0.069247 \n",
"AGE_PERCENTIL_Above 90th -0.069247 1.000000 \n",
"ICU 0.136393 0.105210 \n",
"\n",
" ICU \n",
"AGE_PERCENTIL_70th 0.052805 \n",
"AGE_PERCENTIL_80th 0.117203 \n",
"AGE_PERCENTIL_90th 0.136393 \n",
"AGE_PERCENTIL_Above 90th 0.105210 \n",
"ICU 1.000000 "
]
},
"metadata": {
"tags": []
},
"execution_count": 18
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<Figure size 7200x7200 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dPy6Lz-QcBso",
"outputId": "8c7659e3-d10e-4bbb-8732-a5d669a14781"
},
"source": [
"corr.shape\n",
"ICU_corr = corr.iloc[236]\n",
"ICU_corr.describe()\n"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 201.000000\n",
"mean -0.007311\n",
"std 0.195205\n",
"min -0.561299\n",
"25% -0.126787\n",
"50% 0.044200\n",
"75% 0.103800\n",
"max 1.000000\n",
"Name: ICU, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "02_7tszMigC6",
"outputId": "f82895e5-d26d-480e-9e9d-ecc013db131f"
},
"source": [
"ICU_corr = np.array(ICU_corr)\n",
"selection = []\n",
"for i in ICU_corr:\n",
" if(i):\n",
" if(i>0.11):\n",
" selection.append(True)\n",
" elif(i<-0.12):\n",
" selection.append(True)\n",
" else:\n",
" selection.append(False)\n",
" else:\n",
" selection.append(False)\n",
"\n",
"print(len(selection), selection.count(True))\n",
"selection = np.array(selection)\n",
"selected_final_data = final_data.loc[:, selection]\n",
"selected_final_data.head()\n",
"\n",
"selected_final_data = selected_final_data[['AGE_ABOVE65', 'DISEASE GROUPING 2', 'DISEASE GROUPING 3', 'DISEASE GROUPING 4',\n",
" 'HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN' , 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN',\n",
" 'LACTATE_MEAN', 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN', 'PC02_VENOUS_MEAN',\n",
" 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN', 'SODIUM_MEAN', 'UREA_MEAN', 'BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN', 'BLOODPRESSURE_SISTOLIC_MIN',\n",
" 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN', 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX', 'BLOODPRESSURE_SISTOLIC_MAX',\n",
" 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX', 'BLOODPRESSURE_DIASTOLIC_DIFF', 'BLOODPRESSURE_SISTOLIC_DIFF', \n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF', 'OXYGEN_SATURATION_DIFF', \n",
" 'AGE_PERCENTIL_10th', 'AGE_PERCENTIL_20th', 'AGE_PERCENTIL_80th', 'AGE_PERCENTIL_90th', 'ICU']]\n",
"\n",
"print(selected_final_data.shape)\n",
"selected_final_data.head()\n"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"237 98\n",
"(293, 43)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>HTN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.183673</td>\n",
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" <td>-0.891993</td>\n",
" <td>-0.932246</td>\n",
" <td>1.000000</td>\n",
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" <td>-0.875236</td>\n",
" <td>-0.540721</td>\n",
" <td>0.345679</td>\n",
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" <td>0.086420</td>\n",
" <td>-0.593220</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.000000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.399199</td>\n",
" <td>0.345679</td>\n",
" <td>0.085714</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.551183</td>\n",
" <td>-0.683145</td>\n",
" <td>0.357143</td>\n",
" <td>0.867507</td>\n",
" <td>-0.587500</td>\n",
" <td>-0.145299</td>\n",
" <td>-0.678571</td>\n",
" <td>0.604396</td>\n",
" <td>-0.504274</td>\n",
" <td>-0.627027</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-0.626087</td>\n",
" <td>-0.613497</td>\n",
" <td>-0.572519</td>\n",
" <td>-0.852941</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.878788</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.326531</td>\n",
" <td>-0.968861</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.828421</td>\n",
" <td>-0.729239</td>\n",
" <td>-0.836100</td>\n",
" <td>-0.784714</td>\n",
" <td>-0.779141</td>\n",
" <td>-0.503592</td>\n",
" <td>-0.564753</td>\n",
" <td>0.580247</td>\n",
" <td>0.200000</td>\n",
" <td>-0.937349</td>\n",
" <td>-0.132620</td>\n",
" <td>-0.506215</td>\n",
" <td>-0.119762</td>\n",
" <td>0.652398</td>\n",
" <td>-0.329167</td>\n",
" <td>-0.435897</td>\n",
" <td>-0.547619</td>\n",
" <td>0.216117</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.506306</td>\n",
" <td>-0.263682</td>\n",
" <td>0.754386</td>\n",
" <td>-0.692754</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.582697</td>\n",
" <td>-0.784314</td>\n",
" <td>-0.682540</td>\n",
" <td>-0.723906</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.913659</td>\n",
" <td>-0.851024</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.702202</td>\n",
" <td>-0.641079</td>\n",
" <td>-0.812725</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.990926</td>\n",
" <td>-0.457944</td>\n",
" <td>0.345679</td>\n",
" <td>0.142857</td>\n",
" <td>-0.903614</td>\n",
" <td>0.236838</td>\n",
" <td>-0.539451</td>\n",
" <td>0.189142</td>\n",
" <td>0.852390</td>\n",
" <td>0.000000</td>\n",
" <td>0.048433</td>\n",
" <td>-0.500000</td>\n",
" <td>0.362637</td>\n",
" <td>-0.076923</td>\n",
" <td>-0.293694</td>\n",
" <td>0.019900</td>\n",
" <td>0.912281</td>\n",
" <td>-0.826087</td>\n",
" <td>-0.811861</td>\n",
" <td>-0.725191</td>\n",
" <td>-0.901961</td>\n",
" <td>-0.761905</td>\n",
" <td>-0.959596</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.891012</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.706450</td>\n",
" <td>-0.340249</td>\n",
" <td>-0.846339</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.997732</td>\n",
" <td>-0.292390</td>\n",
" <td>0.345679</td>\n",
" <td>0.085714</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.078189</td>\n",
" <td>-0.495292</td>\n",
" <td>-0.137566</td>\n",
" <td>0.835283</td>\n",
" <td>-0.291667</td>\n",
" <td>-0.418803</td>\n",
" <td>-0.404762</td>\n",
" <td>0.252747</td>\n",
" <td>-0.282051</td>\n",
" <td>-0.553153</td>\n",
" <td>-0.482587</td>\n",
" <td>0.894737</td>\n",
" <td>-0.843478</td>\n",
" <td>-0.820041</td>\n",
" <td>-0.821883</td>\n",
" <td>-0.980392</td>\n",
" <td>-0.904762</td>\n",
" <td>-0.966330</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 1.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 \n",
"\n",
" HTN BIC_VENOUS_MEAN CALCIUM_MEAN CREATININ_MEAN GLUCOSE_MEAN \\\n",
"0 0.0 -0.317073 0.183673 -0.868365 -0.891993 \n",
"4 0.0 -0.317073 0.357143 -0.912243 -0.780261 \n",
"8 0.0 -0.317073 0.326531 -0.968861 -0.891993 \n",
"13 0.0 -0.317073 0.357143 -0.913659 -0.851024 \n",
"18 0.0 -0.317073 0.357143 -0.891012 -0.891993 \n",
"\n",
" INR_MEAN LACTATE_MEAN LEUKOCYTES_MEAN LINFOCITOS_MEAN \\\n",
"0 -0.932246 1.000000 -0.835844 -0.914938 \n",
"4 -0.959849 1.000000 -0.382773 -0.908714 \n",
"8 -0.959849 -0.828421 -0.729239 -0.836100 \n",
"13 -0.959849 1.000000 -0.702202 -0.641079 \n",
"18 -0.959849 1.000000 -0.706450 -0.340249 \n",
"\n",
" NEUTROPHILES_MEAN PC02_VENOUS_MEAN PCR_MEAN PLATELETS_MEAN \\\n",
"0 -0.868747 -0.754601 -0.875236 -0.540721 \n",
"4 -0.412965 -0.754601 -0.939887 -0.399199 \n",
"8 -0.784714 -0.779141 -0.503592 -0.564753 \n",
"13 -0.812725 -0.754601 -0.990926 -0.457944 \n",
"18 -0.846339 -0.754601 -0.997732 -0.292390 \n",
"\n",
" SAT02_VENOUS_MEAN SODIUM_MEAN UREA_MEAN BLOODPRESSURE_DIASTOLIC_MEAN \\\n",
"0 0.345679 -0.028571 -0.836145 0.086420 \n",
"4 0.345679 0.085714 -0.836145 -0.551183 \n",
"8 0.580247 0.200000 -0.937349 -0.132620 \n",
"13 0.345679 0.142857 -0.903614 0.236838 \n",
"18 0.345679 0.085714 -0.884337 -0.078189 \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"0 -0.593220 -0.285714 0.736842 \n",
"4 -0.683145 0.357143 0.867507 \n",
"8 -0.506215 -0.119762 0.652398 \n",
"13 -0.539451 0.189142 0.852390 \n",
"18 -0.495292 -0.137566 0.835283 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.000000 -0.162393 -0.500000 \n",
"4 -0.587500 -0.145299 -0.678571 \n",
"8 -0.329167 -0.435897 -0.547619 \n",
"13 0.000000 0.048433 -0.500000 \n",
"18 -0.291667 -0.418803 -0.404762 \n",
"\n",
" TEMPERATURE_MIN BLOODPRESSURE_DIASTOLIC_MAX BLOODPRESSURE_SISTOLIC_MAX \\\n",
"0 0.208791 -0.247863 -0.459459 \n",
"4 0.604396 -0.504274 -0.627027 \n",
"8 0.216117 -0.247863 -0.506306 \n",
"13 0.362637 -0.076923 -0.293694 \n",
"18 0.252747 -0.282051 -0.553153 \n",
"\n",
" HEART_RATE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.432836 0.736842 -1.000000 \n",
"4 0.000000 1.000000 -0.626087 \n",
"8 -0.263682 0.754386 -0.692754 \n",
"13 0.019900 0.912281 -0.826087 \n",
"18 -0.482587 0.894737 -0.843478 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"4 -0.613497 -0.572519 -0.852941 \n",
"8 -0.730061 -0.582697 -0.784314 \n",
"13 -0.811861 -0.725191 -0.901961 \n",
"18 -0.820041 -0.821883 -0.980392 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF AGE_PERCENTIL_10th \\\n",
"0 -1.000000 -1.000000 0.0 \n",
"4 -1.000000 -0.878788 1.0 \n",
"8 -0.682540 -0.723906 0.0 \n",
"13 -0.761905 -0.959596 1.0 \n",
"18 -0.904762 -0.966330 1.0 \n",
"\n",
" AGE_PERCENTIL_20th AGE_PERCENTIL_80th AGE_PERCENTIL_90th ICU \n",
"0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 "
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "oGzYkVtSypc9",
"outputId": "7cf65310-bb08-4d0f-d049-c76c4dd26a72"
},
"source": [
"corr = selected_final_data.corr()\n",
"corr.shape\n",
"plt.subplots(figsize=(30,30))\n",
"ax = sns.heatmap(\n",
" corr, \n",
" vmin=-1, vmax=1, center=0,\n",
" cmap=sns.diverging_palette(20, 220, n=200),\n",
" square=True\n",
")\n",
"ax.set_xticklabels(\n",
" ax.get_xticklabels(),\n",
" rotation=90,\n",
" horizontalalignment='right'\n",
");\n",
"corr.tail()"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>HTN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <td>-0.313916</td>\n",
" <td>-0.058553</td>\n",
" <td>-0.114311</td>\n",
" <td>-0.054115</td>\n",
" <td>-0.169772</td>\n",
" <td>0.013096</td>\n",
" <td>0.131271</td>\n",
" <td>-0.074361</td>\n",
" <td>-0.107257</td>\n",
" <td>-0.078865</td>\n",
" <td>0.074443</td>\n",
" <td>0.054177</td>\n",
" <td>0.219045</td>\n",
" <td>-0.005714</td>\n",
" <td>0.046349</td>\n",
" <td>-0.148719</td>\n",
" <td>0.084417</td>\n",
" <td>-0.013667</td>\n",
" <td>0.157883</td>\n",
" <td>-0.201817</td>\n",
" <td>-0.047245</td>\n",
" <td>-0.111062</td>\n",
" <td>0.060691</td>\n",
" <td>0.181119</td>\n",
" <td>-0.084997</td>\n",
" <td>0.122856</td>\n",
" <td>-0.084227</td>\n",
" <td>0.073454</td>\n",
" <td>-0.054504</td>\n",
" <td>-0.115735</td>\n",
" <td>0.156086</td>\n",
" <td>0.152179</td>\n",
" <td>-0.013078</td>\n",
" <td>-0.050120</td>\n",
" <td>0.050871</td>\n",
" <td>-0.002497</td>\n",
" <td>-0.022218</td>\n",
" <td>-0.006817</td>\n",
" <td>1.000000</td>\n",
" <td>-0.144480</td>\n",
" <td>-0.116793</td>\n",
" <td>-0.101302</td>\n",
" <td>-0.149620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <td>-0.323780</td>\n",
" <td>0.006130</td>\n",
" <td>-0.081539</td>\n",
" <td>0.015912</td>\n",
" <td>-0.121100</td>\n",
" <td>0.039324</td>\n",
" <td>0.130802</td>\n",
" <td>-0.072781</td>\n",
" <td>-0.056432</td>\n",
" <td>-0.020764</td>\n",
" <td>0.103174</td>\n",
" <td>-0.096160</td>\n",
" <td>0.076510</td>\n",
" <td>-0.117150</td>\n",
" <td>0.038461</td>\n",
" <td>-0.098218</td>\n",
" <td>0.101462</td>\n",
" <td>0.044953</td>\n",
" <td>0.084859</td>\n",
" <td>-0.137249</td>\n",
" <td>0.126126</td>\n",
" <td>-0.077020</td>\n",
" <td>-0.082433</td>\n",
" <td>0.190544</td>\n",
" <td>-0.117213</td>\n",
" <td>0.011360</td>\n",
" <td>-0.109793</td>\n",
" <td>-0.119135</td>\n",
" <td>0.142613</td>\n",
" <td>-0.039946</td>\n",
" <td>0.096577</td>\n",
" <td>0.200298</td>\n",
" <td>0.107792</td>\n",
" <td>0.117468</td>\n",
" <td>0.125407</td>\n",
" <td>0.110745</td>\n",
" <td>0.083120</td>\n",
" <td>0.043650</td>\n",
" <td>-0.144480</td>\n",
" <td>1.000000</td>\n",
" <td>-0.120463</td>\n",
" <td>-0.104485</td>\n",
" <td>-0.140198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <td>0.372051</td>\n",
" <td>-0.048820</td>\n",
" <td>-0.052347</td>\n",
" <td>0.039624</td>\n",
" <td>0.081773</td>\n",
" <td>0.031392</td>\n",
" <td>-0.106084</td>\n",
" <td>0.187437</td>\n",
" <td>-0.006491</td>\n",
" <td>0.066156</td>\n",
" <td>0.072293</td>\n",
" <td>-0.045179</td>\n",
" <td>-0.124717</td>\n",
" <td>-0.017195</td>\n",
" <td>-0.038268</td>\n",
" <td>-0.008421</td>\n",
" <td>-0.176491</td>\n",
" <td>0.062060</td>\n",
" <td>-0.004862</td>\n",
" <td>0.246949</td>\n",
" <td>-0.010905</td>\n",
" <td>0.008452</td>\n",
" <td>-0.009846</td>\n",
" <td>-0.060254</td>\n",
" <td>0.073879</td>\n",
" <td>-0.095074</td>\n",
" <td>0.017877</td>\n",
" <td>0.015329</td>\n",
" <td>-0.057432</td>\n",
" <td>0.036275</td>\n",
" <td>-0.112635</td>\n",
" <td>-0.093839</td>\n",
" <td>-0.092799</td>\n",
" <td>-0.056737</td>\n",
" <td>-0.027386</td>\n",
" <td>-0.048452</td>\n",
" <td>-0.061946</td>\n",
" <td>-0.053039</td>\n",
" <td>-0.116793</td>\n",
" <td>-0.120463</td>\n",
" <td>1.000000</td>\n",
" <td>-0.084463</td>\n",
" <td>0.117203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <td>0.322705</td>\n",
" <td>0.134898</td>\n",
" <td>0.062662</td>\n",
" <td>-0.039135</td>\n",
" <td>0.093065</td>\n",
" <td>-0.023979</td>\n",
" <td>-0.008451</td>\n",
" <td>0.011321</td>\n",
" <td>-0.046688</td>\n",
" <td>0.003333</td>\n",
" <td>-0.157023</td>\n",
" <td>0.067807</td>\n",
" <td>-0.024487</td>\n",
" <td>0.068433</td>\n",
" <td>-0.038205</td>\n",
" <td>0.011468</td>\n",
" <td>-0.031250</td>\n",
" <td>-0.093800</td>\n",
" <td>-0.052278</td>\n",
" <td>0.065191</td>\n",
" <td>0.035194</td>\n",
" <td>0.020020</td>\n",
" <td>0.006909</td>\n",
" <td>-0.033732</td>\n",
" <td>0.227708</td>\n",
" <td>0.005969</td>\n",
" <td>0.028998</td>\n",
" <td>0.042049</td>\n",
" <td>-0.006645</td>\n",
" <td>0.173577</td>\n",
" <td>-0.087677</td>\n",
" <td>-0.093220</td>\n",
" <td>-0.090489</td>\n",
" <td>-0.078546</td>\n",
" <td>-0.137506</td>\n",
" <td>-0.075868</td>\n",
" <td>-0.124175</td>\n",
" <td>-0.028338</td>\n",
" <td>-0.101302</td>\n",
" <td>-0.104485</td>\n",
" <td>-0.084463</td>\n",
" <td>1.000000</td>\n",
" <td>0.136393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ICU</th>\n",
" <td>0.240516</td>\n",
" <td>0.116123</td>\n",
" <td>0.128431</td>\n",
" <td>0.143218</td>\n",
" <td>0.152904</td>\n",
" <td>-0.128091</td>\n",
" <td>-0.281260</td>\n",
" <td>0.219298</td>\n",
" <td>0.186633</td>\n",
" <td>0.125135</td>\n",
" <td>-0.206652</td>\n",
" <td>0.116605</td>\n",
" <td>-0.257798</td>\n",
" <td>0.196802</td>\n",
" <td>-0.151272</td>\n",
" <td>0.342681</td>\n",
" <td>-0.205424</td>\n",
" <td>-0.126787</td>\n",
" <td>-0.270175</td>\n",
" <td>0.284436</td>\n",
" <td>-0.207844</td>\n",
" <td>0.145838</td>\n",
" <td>0.211525</td>\n",
" <td>-0.142736</td>\n",
" <td>0.170446</td>\n",
" <td>0.154358</td>\n",
" <td>0.268190</td>\n",
" <td>0.365030</td>\n",
" <td>-0.374781</td>\n",
" <td>-0.122936</td>\n",
" <td>-0.202084</td>\n",
" <td>-0.285522</td>\n",
" <td>-0.460225</td>\n",
" <td>-0.453095</td>\n",
" <td>-0.521195</td>\n",
" <td>-0.404924</td>\n",
" <td>-0.486424</td>\n",
" <td>-0.232002</td>\n",
" <td>-0.149620</td>\n",
" <td>-0.140198</td>\n",
" <td>0.117203</td>\n",
" <td>0.136393</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n",
"AGE_PERCENTIL_10th -0.313916 -0.058553 -0.114311 \n",
"AGE_PERCENTIL_20th -0.323780 0.006130 -0.081539 \n",
"AGE_PERCENTIL_80th 0.372051 -0.048820 -0.052347 \n",
"AGE_PERCENTIL_90th 0.322705 0.134898 0.062662 \n",
"ICU 0.240516 0.116123 0.128431 \n",
"\n",
" DISEASE GROUPING 4 HTN BIC_VENOUS_MEAN \\\n",
"AGE_PERCENTIL_10th -0.054115 -0.169772 0.013096 \n",
"AGE_PERCENTIL_20th 0.015912 -0.121100 0.039324 \n",
"AGE_PERCENTIL_80th 0.039624 0.081773 0.031392 \n",
"AGE_PERCENTIL_90th -0.039135 0.093065 -0.023979 \n",
"ICU 0.143218 0.152904 -0.128091 \n",
"\n",
" CALCIUM_MEAN CREATININ_MEAN GLUCOSE_MEAN INR_MEAN \\\n",
"AGE_PERCENTIL_10th 0.131271 -0.074361 -0.107257 -0.078865 \n",
"AGE_PERCENTIL_20th 0.130802 -0.072781 -0.056432 -0.020764 \n",
"AGE_PERCENTIL_80th -0.106084 0.187437 -0.006491 0.066156 \n",
"AGE_PERCENTIL_90th -0.008451 0.011321 -0.046688 0.003333 \n",
"ICU -0.281260 0.219298 0.186633 0.125135 \n",
"\n",
" LACTATE_MEAN LEUKOCYTES_MEAN LINFOCITOS_MEAN \\\n",
"AGE_PERCENTIL_10th 0.074443 0.054177 0.219045 \n",
"AGE_PERCENTIL_20th 0.103174 -0.096160 0.076510 \n",
"AGE_PERCENTIL_80th 0.072293 -0.045179 -0.124717 \n",
"AGE_PERCENTIL_90th -0.157023 0.067807 -0.024487 \n",
"ICU -0.206652 0.116605 -0.257798 \n",
"\n",
" NEUTROPHILES_MEAN PC02_VENOUS_MEAN PCR_MEAN \\\n",
"AGE_PERCENTIL_10th -0.005714 0.046349 -0.148719 \n",
"AGE_PERCENTIL_20th -0.117150 0.038461 -0.098218 \n",
"AGE_PERCENTIL_80th -0.017195 -0.038268 -0.008421 \n",
"AGE_PERCENTIL_90th 0.068433 -0.038205 0.011468 \n",
"ICU 0.196802 -0.151272 0.342681 \n",
"\n",
" PLATELETS_MEAN SAT02_VENOUS_MEAN SODIUM_MEAN UREA_MEAN \\\n",
"AGE_PERCENTIL_10th 0.084417 -0.013667 0.157883 -0.201817 \n",
"AGE_PERCENTIL_20th 0.101462 0.044953 0.084859 -0.137249 \n",
"AGE_PERCENTIL_80th -0.176491 0.062060 -0.004862 0.246949 \n",
"AGE_PERCENTIL_90th -0.031250 -0.093800 -0.052278 0.065191 \n",
"ICU -0.205424 -0.126787 -0.270175 0.284436 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MEAN RESPIRATORY_RATE_MEAN \\\n",
"AGE_PERCENTIL_10th -0.047245 -0.111062 \n",
"AGE_PERCENTIL_20th 0.126126 -0.077020 \n",
"AGE_PERCENTIL_80th -0.010905 0.008452 \n",
"AGE_PERCENTIL_90th 0.035194 0.020020 \n",
"ICU -0.207844 0.145838 \n",
"\n",
" TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"AGE_PERCENTIL_10th 0.060691 0.181119 \n",
"AGE_PERCENTIL_20th -0.082433 0.190544 \n",
"AGE_PERCENTIL_80th -0.009846 -0.060254 \n",
"AGE_PERCENTIL_90th 0.006909 -0.033732 \n",
"ICU 0.211525 -0.142736 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN \\\n",
"AGE_PERCENTIL_10th -0.084997 0.122856 \n",
"AGE_PERCENTIL_20th -0.117213 0.011360 \n",
"AGE_PERCENTIL_80th 0.073879 -0.095074 \n",
"AGE_PERCENTIL_90th 0.227708 0.005969 \n",
"ICU 0.170446 0.154358 \n",
"\n",
" RESPIRATORY_RATE_MIN TEMPERATURE_MIN \\\n",
"AGE_PERCENTIL_10th -0.084227 0.073454 \n",
"AGE_PERCENTIL_20th -0.109793 -0.119135 \n",
"AGE_PERCENTIL_80th 0.017877 0.015329 \n",
"AGE_PERCENTIL_90th 0.028998 0.042049 \n",
"ICU 0.268190 0.365030 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_MAX BLOODPRESSURE_SISTOLIC_MAX \\\n",
"AGE_PERCENTIL_10th -0.054504 -0.115735 \n",
"AGE_PERCENTIL_20th 0.142613 -0.039946 \n",
"AGE_PERCENTIL_80th -0.057432 0.036275 \n",
"AGE_PERCENTIL_90th -0.006645 0.173577 \n",
"ICU -0.374781 -0.122936 \n",
"\n",
" HEART_RATE_MAX OXYGEN_SATURATION_MAX \\\n",
"AGE_PERCENTIL_10th 0.156086 0.152179 \n",
"AGE_PERCENTIL_20th 0.096577 0.200298 \n",
"AGE_PERCENTIL_80th -0.112635 -0.093839 \n",
"AGE_PERCENTIL_90th -0.087677 -0.093220 \n",
"ICU -0.202084 -0.285522 \n",
"\n",
" BLOODPRESSURE_DIASTOLIC_DIFF BLOODPRESSURE_SISTOLIC_DIFF \\\n",
"AGE_PERCENTIL_10th -0.013078 -0.050120 \n",
"AGE_PERCENTIL_20th 0.107792 0.117468 \n",
"AGE_PERCENTIL_80th -0.092799 -0.056737 \n",
"AGE_PERCENTIL_90th -0.090489 -0.078546 \n",
"ICU -0.460225 -0.453095 \n",
"\n",
" HEART_RATE_DIFF RESPIRATORY_RATE_DIFF TEMPERATURE_DIFF \\\n",
"AGE_PERCENTIL_10th 0.050871 -0.002497 -0.022218 \n",
"AGE_PERCENTIL_20th 0.125407 0.110745 0.083120 \n",
"AGE_PERCENTIL_80th -0.027386 -0.048452 -0.061946 \n",
"AGE_PERCENTIL_90th -0.137506 -0.075868 -0.124175 \n",
"ICU -0.521195 -0.404924 -0.486424 \n",
"\n",
" OXYGEN_SATURATION_DIFF AGE_PERCENTIL_10th \\\n",
"AGE_PERCENTIL_10th -0.006817 1.000000 \n",
"AGE_PERCENTIL_20th 0.043650 -0.144480 \n",
"AGE_PERCENTIL_80th -0.053039 -0.116793 \n",
"AGE_PERCENTIL_90th -0.028338 -0.101302 \n",
"ICU -0.232002 -0.149620 \n",
"\n",
" AGE_PERCENTIL_20th AGE_PERCENTIL_80th \\\n",
"AGE_PERCENTIL_10th -0.144480 -0.116793 \n",
"AGE_PERCENTIL_20th 1.000000 -0.120463 \n",
"AGE_PERCENTIL_80th -0.120463 1.000000 \n",
"AGE_PERCENTIL_90th -0.104485 -0.084463 \n",
"ICU -0.140198 0.117203 \n",
"\n",
" AGE_PERCENTIL_90th ICU \n",
"AGE_PERCENTIL_10th -0.101302 -0.149620 \n",
"AGE_PERCENTIL_20th -0.104485 -0.140198 \n",
"AGE_PERCENTIL_80th -0.084463 0.117203 \n",
"AGE_PERCENTIL_90th 1.000000 0.136393 \n",
"ICU 0.136393 1.000000 "
]
},
"metadata": {
"tags": []
},
"execution_count": 21
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 2160x2160 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-FiJDAVQ5Nq-",
"outputId": "7b35f915-532c-41dc-8349-b6382da6af72"
},
"source": [
"selected_final_data.columns"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['AGE_ABOVE65', 'DISEASE GROUPING 2', 'DISEASE GROUPING 3',\n",
" 'DISEASE GROUPING 4', 'HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN',\n",
" 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN', 'LACTATE_MEAN',\n",
" 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN',\n",
" 'PC02_VENOUS_MEAN', 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN',\n",
" 'SODIUM_MEAN', 'UREA_MEAN', 'BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
" 'BLOODPRESSURE_SISTOLIC_MIN', 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN',\n",
" 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX',\n",
" 'BLOODPRESSURE_SISTOLIC_MAX', 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX',\n",
" 'BLOODPRESSURE_DIASTOLIC_DIFF', 'BLOODPRESSURE_SISTOLIC_DIFF',\n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF',\n",
" 'OXYGEN_SATURATION_DIFF', 'AGE_PERCENTIL_10th', 'AGE_PERCENTIL_20th',\n",
" 'AGE_PERCENTIL_80th', 'AGE_PERCENTIL_90th', 'ICU'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "bvzM4VbPLli-",
"outputId": "b131c46d-bc15-4e3f-99f9-c254c34dc3e4"
},
"source": [
"Non_ICU_Admitted_data = selected_final_data[selected_final_data['ICU']==0]\n",
"ICU_Admitted_data = selected_final_data[selected_final_data['ICU']==1]\n",
"\n",
"Vital_Non_ICU_Admitted_data = Non_ICU_Admitted_data[['BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
" 'BLOODPRESSURE_SISTOLIC_MIN', 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN',\n",
" 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX',\n",
" 'BLOODPRESSURE_SISTOLIC_MAX', 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX',\n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF']]\n",
"\n",
"Vital_ICU_Admitted_data = ICU_Admitted_data[['BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
" 'BLOODPRESSURE_SISTOLIC_MIN', 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN',\n",
" 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX',\n",
" 'BLOODPRESSURE_SISTOLIC_MAX', 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX',\n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF']]\n",
"\n",
"\n",
"Lab_Non_ICU_Admitted_data = Non_ICU_Admitted_data[['HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN',\n",
" 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN', 'LACTATE_MEAN',\n",
" 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN',\n",
" 'PC02_VENOUS_MEAN', 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN',\n",
" 'SODIUM_MEAN', 'UREA_MEAN']]\n",
"Lab_ICU_Admitted_data = ICU_Admitted_data[['HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN',\n",
" 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN', 'LACTATE_MEAN',\n",
" 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN',\n",
" 'PC02_VENOUS_MEAN', 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN',\n",
" 'SODIUM_MEAN', 'UREA_MEAN']]\n",
"\n",
"\n",
"# set width of bar \n",
"barWidth = 0.25\n",
"fig = plt.subplots(figsize =(20, 10)) \n",
" \n",
"vital_non_ICU = np.array(Vital_Non_ICU_Admitted_data.mean(axis=0)) \n",
"vital_ICU = np.array(Vital_ICU_Admitted_data.mean(axis=0)) \n",
" \n",
"# Set position of bar on X axis \n",
"br1 = np.arange(len(vital_ICU)) + (barWidth*0.5)\n",
"br2 = [x + barWidth for x in br1] \n",
" \n",
"# Make the plot \n",
"plt.bar(br2, vital_ICU, color ='r', width = barWidth, edgecolor ='grey', label ='ICU Admitted') \n",
"plt.bar(br1, vital_non_ICU, color ='b', width = barWidth, edgecolor ='grey', label ='NOT Admitted') \n",
"\n",
" \n",
"plt.xlabel('Features', fontweight ='bold') \n",
"plt.ylabel('Normalized Values', fontweight ='bold') \n",
"plt.xticks([r + barWidth for r in range(len(vital_ICU))], ['BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
" 'BLOODPRESSURE_SISTOLIC_MIN', 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN',\n",
" 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX',\n",
" 'BLOODPRESSURE_SISTOLIC_MAX', 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX',\n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF'], rotation = 90) \n",
"\n",
"plt.legend()\n",
"plt.title(\"Vital Signs of Covid19 Patients\")\n",
"plt.show()\n",
"\n",
"\n",
"# set width of bar \n",
"barWidth = 0.25\n",
"fig = plt.subplots(figsize =(20, 10)) \n",
" \n",
"lab_non_ICU = np.array(Lab_Non_ICU_Admitted_data.mean(axis=0)) \n",
"lab_ICU = np.array(Lab_ICU_Admitted_data.mean(axis=0)) \n",
" \n",
"# Set position of bar on X axis \n",
"br1 = np.arange(len(lab_ICU)) + (barWidth*0.5)\n",
"br2 = [x + barWidth for x in br1] \n",
" \n",
"# Make the plot \n",
"plt.bar(br2, lab_ICU, color ='r', width = barWidth, edgecolor ='grey', label ='ICU Admitted') \n",
"plt.bar(br1, lab_non_ICU, color ='b', width = barWidth, edgecolor ='grey', label ='NOT Admitted') \n",
"\n",
" \n",
"plt.xlabel('Features', fontweight ='bold') \n",
"plt.ylabel('Normalized Value', fontweight ='bold') \n",
"plt.legend()\n",
"plt.xticks([r + barWidth for r in range(len(lab_ICU))], ['HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN',\n",
" 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN', 'LACTATE_MEAN',\n",
" 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN',\n",
" 'PC02_VENOUS_MEAN', 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN',\n",
" 'SODIUM_MEAN', 'UREA_MEAN'], rotation = 90) \n",
"plt.title(\"Lab Test Results of Covid19 patients\")\n",
"plt.show()"
],
"execution_count": 23,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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xy5Yty8DAQI488sg8/PDDefWrX51vf/vbeeYzn5mHH354jb7XWE3qHE+ttcuTXD5k21uGrA8M0+7aJHt1WhwAAACwQbrxxhtXBkHjcfvtt+fOO+/M/vvvn6OPPjoXXXRRTj311Nxwww258MILs3Dhwixfvjz77rvvKufbdNNNs2DBgrz//e/PkUcemRtuuCFPfOIT8/SnPz1/8Rd/kW222WblsfPmzcuZZ56Zyy67LEmy3XbbZcGCBfngBz+YJPmbv/mbHHrooTn33HNz//33Z//998/zn//8nH322XnCE56QW265Jd/5zney7777jvv7Dmcy53gCAAAAWK+MNMH2cNsvuuiiHH300UmSY489NvPn96avvvrqq3PUUUflCU94QrbccsscccQRq7Rbsb7XXntlzz33zPbbb5/p06fnaU97Wm6//fY1qvcLX/hC5s2bl1mzZuWQQw7JI488kp/85Ce56qqr8spXvjJJsvfee2fvvfdeo37Han15qx0AAADAOrHnnnvmq1/96rD7ttlmm9x3332rbLv33nuz7bbbPubY+fPn56677sq//du/JUl++tOf5tZbb13t+adPn54k2WijjVYur1hfvnz5mL9HkrTW8slPfjK/8zu/s0btJooRTwAAAACDHHfccbn22mvzuc99buW2q666KjfeeGP222+/fO1rX8tdd92VpDfn09KlS7Pjjjuu0sf3v//9PPTQQ7njjjuyaNGiLFq0KKeffnrmz5+fgw8+OJ/+9Kfz8MMP58EHH8xnP/vZta51iy22yIMPPjji+mGHHZYPfOADWTF19re+9a0kycEHH7zybXg33nhjvvOd76x1DaMRPAEAAAAMsvnmm+eyyy7LBz7wgey2227ZY4898qEPfSgzZ87Mdtttl/e///05/PDDM2vWrJxyyimZP39+Ntpo1Yhl/vz5Oeqoo1bZ9vKXvzzz58/Pvvvum2OOOSb77LNPXvziF2e//fZb61r33nvvTJs2Lfvss0/e+9735nnPe15uvvnmzJo1KxdddFHe/OY3Z9myZdl7772z55575s1vfnOS5PWvf30eeuih7L777nnLW94yIXNaDadWfVnc1DZ79uy2prPPAwAAAN275ZZbsvvuu69cf9+8eXlg6dIJ63/G9Ok5Zc6cCetvQzX0d0qSqrqhtTZ7uOPN8QQAAAA87giJpgaP2gEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAQJKqyqmnnrpy/cwzz8zAwMDK9XPOOSfPfOYz88xnPjP7779/rrnmmiTJUUcdlVmzZmXXXXfNjBkzMmvWrMyaNSvXXnvtY86xfPnyzJw5M3NGeWvfeeedl5NOOmmNaj/88MNz//335/7778+HPvShldsXLVqUCy64YI36SpITTjghl1xyyRq3G2rjcfcAADxuzJv3vixd+sC4+pg+fUbmzDllgioCAFg7E/HvmsHG8m+c6dOn59///d9z+umnZ9ttt11l32WXXZazzz4711xzTbbddtv813/9V172spflm9/8Zj71qU8lSa688sqceeaZueyyy0Y8xxe/+MU84xnPyMUXX5x3vetdqarxf7kkl19+eZJe0PShD30of/7nf75y/YILLshxxx03IedZU4InAJhCli59IAMDZ4yrj4GBuRNUDQDA2puIf9cMNpZ/42y88cY58cQT8973vjfveMc7Vtn37ne/O+95z3tWBlL77rtvjj/++Jx11ll529veNuY65s+fnze96U358Ic/nOuuuy4HHXRQkuRf/uVf8q53vStbbbVV9tlnn0yfPj1Jb+TR5ptvnm9961v52c9+lnPPPTcf+9jHct111+WAAw7IeeedlyTZeeeds2DBgsyZMyc/+MEPMmvWrLzgBS/I1VdfnVtuuSWzZs3K8ccfnze+8Y2ZM2dOrrzyyixdujRveMMb8md/9mdpreXkk0/OF7/4xey4447ZdNNNx/ydRiN4AgAAAOh7wxvekL333junnXbaKttvuummPOc5z1ll2+zZs3P++eePue9HHnkkX/rSl3L22Wfn/vvvz/z583PQQQflzjvvzBlnnJEbbrghM2bMyPOe97w8+9nPXtnuvvvuy3XXXZdLL700RxxxRL72ta/lIx/5SPbbb78sXLgws2bNWnnsvHnzcuONN2bhwoVJHjsK65xzzsmMGTNy/fXXZ+nSpXnuc5+bF77whfnWt76V733ve7n55ptz9913Z4899shrXvOaNb5+Q5njCQAAAKBvyy23zKte9ar80z/904T3fdlll+V5z3teNt9887z85S/Ppz/96Tz66KP5xje+kUMOOSQzZ87MpptummOOOWaVdi996UtTVdlrr72y3XbbZa+99spGG22UPffcM4sWLVqjGr7whS/kYx/7WGbNmpUDDjggS5Ysya233pqrrroqr3jFKzJt2rQ8+clPzqGHHjoh39mIJwAAAIBBTjnllOy777559atfvXLbHnvskRtuuGGVQOaGG27InnvuOeZ+58+fn2uuuSY777xzkmTJkiW54oorVttuxWN3G2200crlFevLly8f8/mTpLWWD3zgAznssMNW2b5ijqiJZsQTAAAAwCBPfOITc/TRR+ejH/3oym2nnXZa/vqv/zpLlixJkixcuDDnnXfeykm8V+cXv/hFrr766vzkJz/JokWLsmjRopx11lmZP39+DjjggHz1q1/NkiVLsmzZslx88cVrXfsWW2yRBx98cMT1ww47LB/+8IezbNmyJMn3v//9/PKXv8zBBx+ciy66KI8++mjuvPPOfOUrX1nrGgYz4gkAAABgiFNPPTUf/OAHV64fccQRueOOO3LQQQelqrLFFlvk4x//eLbffvsx9fepT30qhx566Cojlo488sicdtpp+fCHP5yBgYEceOCB2WqrrVaZs2lNbbPNNnnuc5+bZz3rWXnxi1+cd77znZk2bVr22WefnHDCCXnTm96URYsWZd99901rLTNnzsynP/3pHHXUUbniiiuyxx57ZKeddsqBBx641jUMVq21CelofTB79uy2YMGCyS4DADozd+7cCXmr3RlnTNwbZAAAxuKWW27J7rvvvnJ93rz3ZenSByas/+nTZ2TOnFMmrL8N1dDfKUmq6obW2uzhjjfiCQAAAHjcERJNDeZ4AgAAAKATgicAAAAAOiF4AgAAAB4XNqR5qNdHa/P7CJ4AAACASbfZZptlyZIlwqfHqdZalixZks0222yN2plcHAAAAJh0O+ywQxYvXpx77rlnskthBJtttll22GGHNWojeAIAAAAm3SabbJJddtllsstggnnUDgAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOTGrwVFUvqqrvVdVtVTVnmP0nVNU9VbWw//mTQfuOr6pb+5/j123lAAAAAKzOxpN14qqaluSsJC9IsjjJ9VV1aWvt5iGHXtRaO2lI2ycmOSPJ7CQtyQ39tvetg9IBAAAAGIPJHPG0f5LbWms/bK39T5ILkxw5xraHJflia+3eftj0xSQv6qhOAAAAANbCZAZPT0ly+6D1xf1tQ728qr5TVZdU1Y5r2BYAAACASfJ4n1z8s0l2bq3tnd6opvPXtIOqOrGqFlTVgnvuuWfCCwQAAABgeJMZPN2RZMdB6zv0t63UWlvSWlvaX/1IkueMte2gPs5prc1urc2eOXPmhBQOAAAAwOpNZvB0fZLdqmqXqto0ybFJLh18QFVtP2j1iCS39Jc/n+SFVbV1VW2d5IX9bQAAAAA8TkzaW+1aa8ur6qT0AqNpSc5trd1UVW9NsqC1dmmSN1bVEUmWJ7k3yQn9tvdW1dvSC6+S5K2ttXvX+ZcAAAAAYESTFjwlSWvt8iSXD9n2lkHLpyc5fYS25yY5t9MCAQAAAFhrj/fJxQEAAABYTwmeAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATgieAAAAAOiE4AkAAACATmw82QUAAI8/c+fOXeu2M6ZPzylz5kxgNQAArK8ETwDAY5wxMLDWbeeOoy0AAFOLR+0AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBObDzZBQBrb96892Xp0gfG1cf06TMyZ84pE1QRAAAA/IbgCdZjS5c+kIGBM8bVx8DA3AmqBgAAAFblUTsAAAAAOiF4AgAAAKATgicAAAAAOiF4AgAAAKATJhcHAABgg+Ct0LDuCZ4AAADYIHgrNKx7HrUDAAAAoBOCJwAAAAA6IXgCAAAAoBOCJwAAAAA6IXgCAAAAoBOCJwAAAAA6sfFkFwBMvrlz1/6VsDOmT88pc+ZMYDUAAABMFYInIGcMDKx127njaAsAAMDU5lE7AAAAADoheAIAAACgE4InAAAAADoheAIAAACgE4InAAAAADoheAIAAACgE4InAAAAADoheAIAAACgE4InAAAAADoheAIAAACgE5MaPFXVi6rqe1V1W1XNGWb/X1bVzVX1nar6clU9ddC+R6tqYf9z6bqtHAAAAIDV2XiyTlxV05KcleQFSRYnub6qLm2t3TzosG8lmd1a+1VVvT7J3yc5pr/v4dbarHVaNAAAAABjNmnBU5L9k9zWWvthklTVhUmOTLIyeGqtfWXQ8V9P8sp1WiEAAAAMMXfu3LVuO2P69Jwy5zEP/MCUNZnB01OS3D5ofXGSA0Y5/rVJ/mPQ+mZVtSDJ8iTzWmufHq5RVZ2Y5MQk2WmnncZVMAAAAJwxMLDWbeeOoy2sjyYzeBqzqnplktlJfm/Q5qe21u6oqqcluaKqvtta+8HQtq21c5KckySzZ89u66Tg9cD75s3LA0uXrnV7KT0AAACwOpMZPN2RZMdB6zv0t62iqp6f5G+T/F5rbWVS0lq7o//3h1V1ZZJnJ3lM8MTwHli6VEoPAAAAdGoy32p3fZLdqmqXqto0ybFJVnk7XVU9O8nZSY5orf1s0Patq2p6f3nbJM/NoLmhAAAAAJh8kzbiqbW2vKpOSvL5JNOSnNtau6mq3ppkQWvt0iTvSfJbSS6uqiT5SWvtiCS7Jzm7qn6dXng2b8jb8AAAAACYZJM6x1Nr7fIklw/Z9pZBy88fod21SfbqtjoAAAAAxmMyH7UDAAAAYAoTPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ0QPAEAAADQCcETAAAAAJ3YeLILYO3Mm/e+LF36wFq3X7Zs2gRWAwAAAPBYgqf11NKlD2Rg4Iy1bj8wMHcCqwEAAPhZp2EAACAASURBVAB4LI/aAQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANAJwRMAAAAAnRA8AQAAANCJMQVPVfUHVfW+qtqjql5ZVft0XRgAAAAA67fVBk9VdUqSzyY5OcmTkvx/Sd7TcV0AAAAArOfGMuLplCQXD1r/UpJ9uykHAAAAgKliLMHT1km+PWj9CUmmdVMOAAAAAFPFxmM45htJXt9f/t9J/leSr3VWEQAAAABTwlhGPL0pycNJKsmLktyZ3uN3AAAAADCi1QZPrbVbkuye5Fn9zx6tte9NxMmr6kVV9b2quq2q5gyzf3pVXdTf/42q2nnQvtP7279XVYdNRD0AAAAATJzVPmpXVW8ZZltrrb1tPCeuqmlJzkrygiSLk1xfVZe21m4edNhrk9zXWtu1qo5N8u4kx1TVHkmOTbJnkicn+VJVPaO19uh4agIAAABg4oxljqeBYba1JOMKnpLsn+S21toPk6SqLkxyZJLBwdORg85/SZIPVlX1t1/YWlua5EdVdVu/v+vGWRMAAAAAE2QswdPR6QVNSe8Nd29Icv0EnPspSW4ftL44yQEjHdNaW15VDyTZpr/960PaPmUCagIAAABggqw2eGqtXTJ4vap+mfGPdlpnqurEJCcmyU477TTJ1Uyc6dNnZGBg7lq3f3T5xpk7MLDW7WdMn77WbSfKvHnvy9KlD6x1+0eXb5xpGy8fVw0zpk/PKXMeMz3ZOjPe+yBxLyRT414Y7zVIxn8dJvsaJO6FxH8XVpjse2Gy74PEfxdWcC9M/jVIXIfENVhhsq+D/53scS/438l1qrU26ifJdwZ9bk7vDXd3r67dGPo9MMnnB62fnuT0Icd8PsmB/eWNk/w8vbfrrXLs4ONG+zznOc9pTB0DAwMtaWv9GRgYWPvG/c/AwMBkXwaae2EirsFEXIfJvgYTcR2mwr1Az2TfC4+H+8B/FybmOrgGU+e/jZN9HVyDx891wL0wEddgqvx3YaIkWdDa8FnMat9ql9+8ze5ZSZ6R5I4kfz7+yCvXJ9mtqnapqk3Tmyz80iHHXJrk+P7yHya5ov+FLk1ybP+td7sk2S3JNyegJgAAAAAmyFgetRtLOLXGWm/OppPSG600Lcm5rbWbquqt6SVllyb5aJJ/7U8efm964VT6x30ivRFYy5O8oXmjHQAAAMDjyojBU1X95SjtWmvtveM9eWvt8iSXD9n2lkHLjyT5oxHaviPJO8ZbAwAAAADdGG3E05lJWnpzKg3Vkow7eAIAAABg6hoteHr1OqsCAAAAgClnxOCptXb+uiwEAAAAgKlltROHV9XuVfX5qvppVd3b/yxZF8UBAAAAsP4ayxvrzk7yu0melOShJFslWdxlUQAAAACs/8YSPD07yd+nN6H4q5O8Pcn1XRYFAAAAwPpvtMnFB/tp/++LkmyT5Ogkf9JJRQAAAEBnpk+fkYGBuWvdftmyaRNYDVPdWIKnW5M8Jcl1SU5Nb+STEU8AAACwHpoz55RxtZ87d+1DKzY8IwZPVXVokq8keWGSXyf5aJI3pRc8fWCdVAcAAADAemu0EU9fSnJ3kk8m+URr7aokc9ZJVQAAAACs90abXPy8JJsk+fMkX6mqO6rq/VV10DqpDAAAAID12ojBU2vtNUm2S+9Ru//TP/bkJFdX1U/WTXkAAAAArK9GG/GU1tqjrbUvtdZel+T303v8rtKbbBwAAAAARjTqW+2qas8kf9j/7JFe6PRwks91XxoAAAAA67PR3mp3S5JnpBc2PZLkM0kuSvLZ1tqv1k15AAAAAKyvRhvx9LQklyX5RJLPtNYeWjclAQAAADAVjBY8zWyt/WKdVQIAAADAlDLaW+2ETgAAAACstVHfagcAAAAAa0vwBAAAAEAnRnur3atGa9ha+9jElwMAAADAVDHa5OLnJWmj7Bc8AQAAADCi0YKnv+r/3SvJ85Ocm96jeSckubLTqgAAAABY740YPLXW/iFJqurGJAOttY/013+c5KR1Ux4AAAAA66vRRjytsF2Sk6tqeXojnk5O8qROqwIAAABgvTeW4OndSf4+yUeTVH/b/+6sIgAAAACmhNUGT621M6vqC0kO6W+6srX2nU6rAgAAAGC9t9EaHntZkhlVtWNH9QAAAAAwRax2xFNVHZvk4+k9ZvedJKcn+WWSl3VbGgAAAADrs7GMeJqb5MuD1j+X5MBuygEAAABgqhhL8PTkJFcMWl+WZPNuygEAAABgqhjLW+2+m+RV/eX/P8mLkny7s4oAAAAAmBLGMuLp1CRPSm+Op+OTbJLkf3dZFAAAAADrv9WOeGqtXVdVu+Y38zpd11q7r9uyAAAAAFjfrXbEU1VdkeSvk/xHa+3yJLP62wAAAABgRGOZ4+mQJL+XZM+qOjbJb/fXAQAAAGBEY5njKUk+m+SFSa5O8pTuygEAAABgqhhr8HRBksOT7JLk3d2VAwAAAMBUMdbgKa21Lyd5bpLF3ZUDAAAAwFQxlrfabTRo+eaqelaSbTutCgAAAHhcmjF9euYODIy7DzYMIwZPVfVPSc5N8pphdrckb+qqKAAAAODx6ZQ5cya7BNYjo414OinJNf2/QwmeAAAAABjVaMHT85Lc3P8LAAAAAGtktODpu0P+AgAAAMCYjRY8/Ty9R+qG01bTFgAAAIAN3Gjh0VUZOXgCAAAAgFGNGDy11g5Zh3UAAAAAMMWs9nG5qqokxybZK8lm/c2ttXZql4UBAAAAsH4byzxNZyV5XXqP3VV/W0sieAIAAABgRBuN4ZijklzQX35Tkq8keVtnFQEAAAAwJYwleNo6ydX95TuTXJLkxM4qAgAAAGBKGMujdnf1j7sryUeSbJrkF10WBQAAAMD6bywjnv4uyQ/Sm9PpkSQPJDmly6IAAAAAWP+tdsRTa+3jg1Yv7LAWAAAAAKaQ1QZPVTUryVuS7JJkWn9za63t02VhAAAAAKzfxjLH0yVJntZ1IQAAAABMLWOZ42nL9EY8bdxa22jFp+O6AAAAAFjPjWXE05vSm1h8cVWteJtda619qruyAAAAAFjfjSV4en6SfZN8tL9eSVp+M98TAAAAADzGWIKnP0pyQ5LPJ1nWbTkAAAAATBVjCZ7+M8lNrbW5XRcDAAAAwNQxluBpnyQvr6rXJrm/v6211vbpriwAAAAA1ndjCZ526//dof8BAAAAgNUaS/C0dZKlrbVHui4GAAAAgKljo9F2VlUluSPJq9ZNOQAAAABMFaMGT621luTyJLPXTTkAAAAATBWjBk99s5K8tqrurqrv9D/fHs9Jq+qJVfXFqrq1/3frYY6ZVVXXVdVN/XMeM2jfeVX1o6pa2P/MGk89AAAAAEy8sQRPuyapJDOTPKv/2Wuc552T5Muttd2SfLm/PtSvkryqtbZnkhcleV9VbTVo/1+11mb1PwvHWQ8AAAAAE2y1wVNrbaPhPuM875FJzu8vn5/kZcOc9/uttVv7yz9N8rP0wi8AAAAA1gNjCpCq6oSquqiqLqyqiZhofLvW2p395buSbLea8++fZNMkPxi0+R39R/DeW1XTJ6AmAAAAACbQxqs7oKr+LslbB236o6raobX2ztW0+1KSJw2z628Hr7TWWlW1UfrZPsm/Jjm+tfbr/ubT0wusNk1yTpK/HlLj4PYnJjkxSXbaaafRSgYAAABgAo1lxNOfJPlskmf0P5elH+SMprX2/Nbas4b5fCbJ3f1AaUWw9LPh+qiqLZN8Lsnftta+PqjvO1vP0iT/kmT/Ueo4p7U2u7U2e+ZMT+oBAAAArCtjCZ62TvLF1tptrbXbknyxv208Lk1yfH/5+CSfGXpAVW2a5FNJPtZau2TIvhWhVaU3P9SN46wHAAAAgAm22kftkixI8s7+PEtJb2Lw68d53nlJPlFVr03y4yRHJ0lVzU7yutban/S3HZxkm6o6od/uhP4b7P6tqmam97a9hUleN856AAAAAJhgYwme3pjeCKVX9td/kOTk8Zy0tbYkye8Ps31Beo/2pbX28SQfH6H9oeM5PwAAAADdW23w1Fq7qap+J8nv9Dd9r7W2vNuyAAAAAFjfjRg8VdVfjrDrsKpKa+0fO6oJAAAAgClgtBFPZyZp6c2jtEIbtCx4AgAAAGBEowVPrx6yvnt6k3hvmeSnnVUEAAAAwJSw0Ug7Wmvnt9bOT/LdJEck+askP08vfHrauikPAAAAgPXViMFTVR1cVf+Z5Poku6b3VrvdWmvntNb+Z10VCAAAAMD6abRH7a5Mb06nXyT5SZJXJHlFVSVJa60d2Xl1AAAAAKy3Rguekt7E4jOS/MGQ7W2YYwEAAABgpdGCp13WWRUAAAAATDkjBk+ttR+vy0IAAAAAmFpGnFwcAAAAAMZD8AQAAABAJwRPAAAAAHRC8AQAAABAJ0Z7qx08rk2fPiMDA3PXuv2yZdMmsBoAAABgKMET6605c04ZV/u5c9c+tAIAAABWz6N2AAAAAHRC8AQAAABAJwRPAAAAAHRC8AQAAABAJ0wuzgZrxvTpmTswMO4+AAAAgOEJnthgnTJnzmSXAAAAAFOaR+0AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBOCJ4AAAAA6ITgCQAAAIBObDzZBQAAANC96dNnZGBg7lq3X7Zs2gRWA2woBE8AAAAbgDlzThlX+7lz1z60AjZcHrUDAAAAoBOCJwAAAAA6IXgCAAAAoBOCJwAAAAA6MSnBU1U9saq+WFW39v9uPcJxj1bVwv7n0kHbd6mqb1TVbVV1UVVtuu6qBwAAAGAsJmvE05wkX26t7Zbky/314TzcWpvV/xwxaPu7k7y3tbZrkvuSvLbbcgEAAABYU5MVPB2Z5Pz+8vlJXjbWhlVVSQ5NcsnatAcAAABg3Zis4Gm71tqd/eW7kmw3wnGbVdWCqvp6Va0Il7ZJcn9rbXl/fXGSp3RYKwAAAABrYeOuOq6qLyV50jC7/nbwSmutVVUboZunttbuqKqnJbmiqr6b5IE1rOPEJCcmyU477bQmTQEAAAAYh86Cp9ba80faV1V3V9X2rbU7q2r7JD8boY87+n9/WFVXJnl2kk8m2aqqNu6PetohyR2j1HFOknOSZPbs2SMFXAAAAABMsMl61O7SJMf3l49P8pmhB1TV1lU1vb+8bZLnJrm5tdaSfCXJH47WHgAAAIDJNVnB07wkL6iqW5M8v7/+f9m797jr6jn/4693pVvoSBoSKqdxqOhmHMahkvOhEOVM+M0MRhqU01T8NEOM4/zML2OI35AYaQxjUjkPQ4gUpqiIQeWQYwd9fn+stbv3fd3Xdd2p9vpe916v5+NxPVqHfV199ufea+21Put7IMnqJP/Yv+aPgVOTfI2u0PS3VXVmv+8Q4OAkZ9ON+fT2QaOXJEmSJEnSes2sq91yquoiYK9Ftp8KPKNf/k/gTkv8/neBu80yRkmSJEmSJF0zrVo8SZIkSZIkac5ZeJIkSZIkSdJMWHiSJEmSJEnSTFh4kiRJkiRJ0kxYeJIkSZIkSdJMWHiSJEmSJEnSTFh4kiRJkiRJ0kxYeJIkSZIkSdJMWHiSJEmSJEnSTGzSOgBJkiRJ0sq35apVHHH44df4b0gaFwtPkiRJkqT1OujQQ1uHIGkDZFc7SZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNhIUnSZIkSZIkzYSFJ0mSJEmSJM2EhSdJkiRJkiTNxCatA5AkSdK1b9WqLTn88COu0d+47LKNr6VoJEnSWFl4kiRJmkOHHnrQNf4bRxxxzQpXkiRJdrWTJEmSJEnSTFh4kiRJkiRJ0kxYeJIkSZIkSdJMOMaTJEmSJF0FRxx++NX+3S1Xrbr2ApGkDYiFJ0mSJEm6Cg477LDWIUjSBseudpIkSZIkSZqJJoWnJNsk+XiSs/r/br3Ia/ZIctrUz++S7NPve2eSc6b27Tb8u5AkSZIkSdJyWrV4OhQ4uapuDZzcr6+lqj5RVbtV1W7AnsBvgBOnXvLCyf6qOm2QqCVJkiRJknSVtSo8PRI4pl8+BthnPa9/DPDvVfWbmUYlSZIkSZKka02rwtN2VfU//fKPgO3W8/r9gfcu2PaqJF9P8vokS04RkeRZSU5NcuoFF1xwDUKWJEmSJEnSH2Jms9olOQn4o0V2vXR6paoqSS3zd24C3An4j6nNL6YrWG0KHA0cArxisd+vqqP717B69eol/z+SJEnTnDZdkiTpmptZ4amq7r/UviQ/TnKTqvqfvrD0k2X+1GOB46vqsqm/PWktdUmSdwAvuFaCliRJ6jltuubFqlVbcvjhR1zt37/sso2vxWgkSWMzs8LTevwr8BTgb/v/nrDMaw+ga+F0pamiVejGh/rGrAKVJEmSNmSHHnrQNfr9I464+kUrSZJajfH0t8DeSc4C7t+vk2R1kn+cvCjJLYEdgE8t+P1/TnI6cDpwI+B/DxCzJEmSJEmS/gBNWjxV1UXAXotsPxV4xtT6ucD2i7xuz1nGJ0mSJEmSpGuuVYsnSZIkSZIkzblWYzxJkiRJ2gBsuWrVNZrlcfI3JEnjZOFJkiRJ0pIOOvTQ1iFIkjZgdrWTJEmSJEnSTNjiSZIkzZ1Vq7bk8MOv/hTwq1ZteS1GI0mSNF4WniRJ0tw59NCDWocgSZIk7GonSZIkSZKkGbHFkyRJkhZ1TWczcyYzSZJk4UmSJEmLcjYzSZJ0TdnVTpIkSZIkSTNh4UmSJEmSJEkzYVc7SZIkaRmOcyVJ0tVn4UmSJElaxmGHHdY6BEmSNlh2tZMkSZIkSdJMWHiSJEmSJEnSTFh4kiRJkiRJ0kxYeJIkSZIkSdJMOLi4JEmSJEnS1eDMp+tn4UmSJEmSJOlqcObT9bOrnSRJkiRJkmbCFk+SpCvZVFiSJEnStcnCkyTpSjYVliRJknRtsqudJEmSJEmSZsLCkyRJkiRJkmbCwpMkSZIkSZJmwjGeJEmSNLdWrdqSww8/4hr9viRJuvosPEmSJGluHXroQa1DkCRp1OxqJ0mSJEmSpJmwxZMkAUccfvg1+v0tV626dgKRJEmSpDli4UmSgMMOO6x1CJIkSZI0dyw8SZIkSZKkUbmmk09M/obWz8KTJM0BvzglSZKkq87JJ4Zj4UmS5oBfnJIkSZJWIme1kyRJkiRJ0kzY4kmSJEnS3Lum3dLtki5JV4+FJ0mSJElzz27pktSGXe0kSZIkSZI0ExaeJEmSJEmSNBMWniRJkiRJkjQTFp4kSZIkSZI0ExaeJEmSJEmSNBMWniRJkiRJkjQTm7QOQJKuqVWrtuTww4+4Rr8vSZIkSbr2WXiStME79NCDWocgSZIkSVqEXe0kSZIkSZI0ExaeJEmSJEmSNBMWniRJkiRJkjQTFp4kSZIkSZI0ExaeJEmSJEmSNBMWniRJkiRJkjQTm7QOQJKkleSIww+/Rr+/5apV104gkiRJ0hyw8CRJ0pTDDjusdQiSJEnS3LCrnSRJkiRJkmbCwpMkSZIkSZJmwq52kqS5sWrVlhx++BHX6PclSZIkXXssPEmS5sahhx7UOgRJkiRJU+xqJ0mSJEmSpJmw8CRJkiRJkqSZsPAkSZIkSZKkmbDwJEmSJEmSpJmw8CRJkiRJkqSZsPAkSZIkSZKkmbDwJEmSJEmSpJmw8CRJkiRJkqSZsPAkSZIkSZKkmbDwJEmSJEmSpJloUnhKsl+SM5JckWT1Mq97UJJvJzk7yaFT23dM8l/99vcl2XSYyCVJkiRJknRVtWrx9A3gUcCnl3pBko2BvwceDNweOCDJ7fvdrwZeX1W3An4GHDjbcCVJkiRJkvSHalJ4qqpvVtW31/OyuwFnV9V3q+pS4FjgkUkC7Al8oH/dMcA+s4tWkiRJkiRJV8dKHuNpe+D7U+vn99tuCPy8qi5fsH1RSZ6V5NQkp15wwQUzC1aSJEmSJElr22RWfzjJScAfLbLrpVV1wqz+vwtV1dHA0QCrV6+uof6/kiRJkiRJYzezwlNV3f8a/okfADtMrd+s33YRsFWSTfpWT5PtkiRJkiRJWkFWcle7LwG37mew2xTYH/jXqirgE8Bj+tc9BRisBZUkSZIkSZKumiaFpyT7JjkfuAfwkST/0W+/aZKPAvStmZ4D/AfwTeC4qjqj/xOHAAcnOZtuzKe3D/0eJEmSJEmStLx0DYjGYfXq1XXqqae2DkOSJEmSJGluJPlyVa1ebN9K7monSZIkSZKkDZiFJ0mSJEmSJM2EhSdJkiRJkiTNxKjGeEpyAXBe6zhWiBsBF7YOojFz0DEP5mDCPJiDCfNgDibMgzkAczBhHszBhHkwBxPmYY1bVNW2i+0YVeFJayQ5damBv8bCHHTMgzmYMA/mYMI8mIMJ82AOwBxMmAdzMGEezMGEebhq7GonSZIkSZKkmbDwJEmSJEmSpJmw8DReR7cOYAUwBx3zYA4mzIM5mDAP5mDCPJgDMAcT5sEcTJgHczBhHq4Cx3iSJEmSJEnSTNjiSZIkSZIkSTNh4UmSJEmSJEkzYeFJkiRJkiRJM2HhSZIkSZIkSTOxSesAJA0nyX2W219Vnx4qllaS3Hy5/VX1vaFiaSnJXy+zu6rqlYMF05B58LwwkeTJy+2vqncNFUsr5sAcSAt5TJgDrZHkHcBSs7NVVR04ZDwbEme1G4Ekv2TNAZL+v0VXeNy0qkZRgExyDmufKDK1XlW18/BRDSvJhxfZXMAuwA5VtfHAIQ0uyel07zlTmwvYFrjxGHIAkOSvFtl8PeAZwA2r6gYDh9SEefC8MJHkzUvsegSw/Ri+K82BOZhI8gmWv7naa8h4WvD6ueMxYQ7A+6iJJI9eZPMOwPOBjavqZgOHtMGw8DRCSW4APBv4X8DxVbXYjdfcSXLDBZs2Ah4LvAD4SlUtdiKZa0nuBbwM2Bp4VVUtdgM615LcEjgEuD/wpqpa6uJibiXZHHgecCBwHPC6qvpJ26iGZx46nhcgSYAn0J0bzqTLw9fbRjUsczDuHCTZfZHNdwdeBPykqu46cEjNjfX6edqYj4mJsebA+6h1JdkJeAlwH+D1wNur6tK2Ua1cc1+d1RpJtgIOAp4MvAe4a1Vd1Daq4Uzea5KNgCcBLwROAx5aVWe2jG1oSfYCXk73pOLIqvp445AGl+TWwEuBPwFeB/xlVV3WNqphJdkGOJjuAuoY4C5V9bO2UQ3PPHQ8L0CSTYCn0l1IfwF4TFV9u2lQAzMH5gCgqr48WU5yX7pzw3WBP6uqf28WWANjv34GjwkwB95HrZHkdnQP6O4MHEV3Xry8bVQrn4WnEUhyI+CvgMcB/wTcuap+0Taq4SW5DvB0uqaQnwX2qaqz20Y1rCQPpSu2/AJ4WVV9tnFIg0tyR7oc3AF4DXBgVf2+bVTDS3IU8CjgaOBOVfWrxiE1YR48L0wkeTZdi7eTgQdV1bltIxqeOTAH05I8kO7m6hK6Vh2faBzSoLx+7nhMmAPwPmoiyfuB3ekeWj8f+D2wRdcQDqrqp+2iW9nsajcCSX4NXAC8A/jlwv1V9XeDB9VAkvOBy4E3AOsMIF1VHxw8qIEluQI4H/gai4zdUFWPGDyogSX5PfB94CN0XxZrqaq/HDyoBvrPwiV0x8Q6ffaraosmgQ3MPHhemOjz8BO678vFPgu7NAlsQObAHEwk+RLd2IdHAZ9fuL+qvjJ4UAPz+rnjMWEOwPuoiSTnMjW21WTzZL2qdho8qA2ELZ7G4TVTy5s3i6K9k+hOELv2P9MKGMMJc4/WAawAT28dwEpQVRu1jmElMA+A54WJHVsHsAKYA3Mw8WvgV8BjgEez7oQce7YIamBHsebGcuH185ie3HtMmAPwPgqAqrpl6xg2VLZ4GoEkz6mqt7SOYyVLsl1V/bh1HK0k2QHYv6qOah1LK0mu9k0Q+wAAIABJREFUCzy8qt7fOpZWklwf2Bc4oKoe2jqeVsxDx/NCJ8mf0n0Wnt06llbMgTmYluQ6YxsTcaEkd62qL7WOoyWPCXMw4X1UdgYeT3fNdIfW8axUPukdB1t4LCLJVkkOTHIy8NXW8QwtybZJ/iLJZ4BPAts1DmlwSTZO8pAk7wbOoxvHYVSSbJpk377P+v8AewH/0DiswZmHjueFTpI7Jzmqb1L/SuBbjUManDkwB9PS2SvJ2+m65o5OktsneWWSs4G3to6nBY8JczDhfVRumuT5fbfkM+jqKvs3DmtFs6udRiXJZsAj6arSd6ZrOr0P8OmWcQ0l3XTxj6J7/7ehaxa7Y1XdrGlgA+tn6Hk88BDgi8C96PLwm6aBDSjJA4ADgAcAnwDeRTdTz9OaBjYw8+B5YSLJbeg+CwcAFwLvo2sZPpquiObAHCyU5O5054Z9gG2AZ9PN6jUKSW7Jms/DZcAtgNVjGlzaY8IcTIz9PgogybPoPgfbA8cBBwInVNURTQPbANjVbgSSXA4sdkM9msFzAZK8B7g3cCJwLHAKcHZVjabfdpLf0hVaXgZ8tqoqyXfHNBBePzji9+ieVn6oqn6Z5JwxfQ7gyoEyPwM8tarO6beN6rMA5gE8L0xMfRYOnMzUM7Y8mANzMJHkSGA/uu/L9wLHA6eO6bsyyeeBLeiuGY+tqrNGfr0w2mPCHHgfNZHkUroJF/6qqk7tt43qs3B12dVuHE6vqi0W+dl8LEWn3u2BnwHfBL5ZVb9nXINDArwYWAX8H+DFfZ/ksfkAcFO6bnUP78fzGdvnAOAudF+cJyX5eJIDgY0bx9SCefC8MPEoum6Wn0jytiR7sfaAymNgDszBxDOAH9M9pHl3VV3E+L4rf0zXomM7uhn+YHw5AI8JMAfgfdTETeiK8a9L8u0krwSu0zimDYItnkYgyVer6s6t41gJktyOrnnk4+iayt4WuOPYBsRLshNdP+QDgFsDhwHHV9V/Nw1sIEkC3I/u/T8E2JKuqexHq+pXDUNrIsk96XLxaOBrdJ+Fo9tGNbyx52Hs54WJvhj9SLo87EnX/fL4qjqxaWADMgfmIMnGwN50738vuq7I9wd2qKrLW8Y2pCRb0hUdJufFrYAHVtUXmwbWwNiPCTAH3ketLcnN6HJxAHB9us/CS9pGtXJZeBqBJC+pqiNbx7HSJNmd7kTxWOD8qrpn45CaSHJH+i+RqrpV63iGluQ6wAPpcvDAqrpR45CaSbIR3Y3F46rqwNbxtGIePC9MJNmarrvR46pqr9bxtGAOzEGSVcDD6M4J9wZOrqrHt41qeEluTHfNeABw86raoXFIzYz9mABz4H3U2vpxwPavqle0jmWlsvA0AknezDJNIavqLwcMZ8XpW7/cu6rmfmC8JLerqm/1y6uq6pKpffeoqs+3i24YSbaoqouX2PfHVfXNoWNqIckTq+r/9cv3qqrPTe17TlW9pV10wzEPnhcmkuxZVaf0yztOxvzq1x9dVf/SLrphmANzsD5JtgD2qap3tY6lpSS3rqqzWscxBI8Jc7CcMd1HASR5UVW9pl/er6reP7XvSFs8Lc0xnsbhVODL/c8jppYnP6OQ5Lip5VdPlqurvr6sSVDDe8/U8sKbyb8fMpCGPjlZSDcF7LR/HjaUpg6eWn7zgn1PHzKQxsyD54WJ104tL7yJeOmQgTRkDswBAEneMLX8vMly/+BmzyZBDSzJZ6eW371g9/sGDqcljwlz4H3UGvtPLb94wb4HDRnIhmaT1gFo9qrqmMlykoOm10fm1lPLewOHTK1vyzhkieXF1ufV9PvcZpl9887PQsc8mIMJ82AOwBxM3Gdq+SnAG6fWdxk4llauP7V8hwX7xvRZ8JgwB+B91ISfhavJFk/jM+a+lcu997HkpZZYXmx9XpmDjnnomAdzMGEezAGYg4nlbq7GwuvGjseEOQCPhwk/C1eTLZ40JtdLcme6gutm/XL6n82aRjacmyV5E917nizTr2/fLqxB3TjJwXTvebJMvz6mJza3S/J1uve9c79Mv75Tu7AGZx48L0zslORf6f/t+2X69R3bhTUoc2AOJjbqB0/eaGp5UoDauF1Yg9oqyb50OdgqyaP67aGbDXcsPCbMAXgfNbFrkovp33e/TL9+3XZhrXwOLj4CSX7Jmgrs9YDfTHbRdc3doklgA0vySZYfZH2P4aJpI8lTlts/hm6YSQ5bbn9VHTFULC0lucVy+6vqvKFiack8eF6YSHLf5fZX1aeGiqUVc2AOJpKcC1zB4q2dqqrmvjCf5B3L7a+qpw0VS0seE+YAvI/SNWfhSZIkSZIkaRlJFo4Pu5aq+ulQsWxoLDxpNKaaSC+qqj44VCytTDUNXlRVPWKoWFqZ6ka0qKr6y6FiaWlBS0joW0AyvpaQo8+D54XOVDfLRVXV3A+obA7MwUSSuyy3v6q+MlQsrUx1xV9UVf3dULG05DFhDsD7qIkkVwDnA5dPNk3tHkVr0KvLMZ40Jh8ATut/YMGJAhjDCfMewPeB9wL/xTgHDP0z4BvAccAPGWcOAE4G/ojuc39sVX2vcTytmAfPCxNX0H0XvAf4MPDbtuE0YQ7MwcSpdN+VF/brC6+Z9hw8ouG9lu6a8d+BS/DcOOZjwhx4HzXxJmAP4HN0102fLVvyXCW2eNJoJNkH2B+4FXAC8N6qOrttVMNKsjHdFKgH0E2H/BG6PJzRNLABJbkhsB/wOLqnFe8DPlBVP28aWANJtgQeRXdcXJcuF8eOrZnw2PPgeWGNJLejy8PDgTPpbjJOrKrLl/3FOWIOzAFAkoOAxwC/AI4Fjq+qX7WNalhJdqX7HDwI+DLdTebJY7zJ9JgwB95HrZEkwP3oPg93A04E3lpV57SMa6Wz8KTRSXJ94JF0hYcbAi8dw6CACyVZRXfCPAo4oqre0jikwSW5Gd2X6MHAIVX17sYhNZFkI7o8vAk4cizdBxYyD54XpiV5HPD3wKur6qjW8bRgDsxBkp3ozouPBM6jOzeetvxvzZ8k96Q7N96f7nph2S7K82zsxwSMOwfeR62RZCu68+MrgZdU1dsah7Si2dVOY/Q7uid4FwO3YGRTX/Y3lg+lu4C6Jd1N9vEtY2qhH7/iALqWHv9O9zRzVKYupO8NfBbYt6o+0zaq4ZkHzwsTSbanu4jcF/gZ8HxGlgdzYA6mVdV3k5xAN136k4DbsKarzSgk2Ra4M3AnurFdftI2ouF5TJiDKWO/j5ouvG1L18Vw95EO1fAHscWTRiPJnnRfGHcDTqLrSnNq26iGleRdwB2Bj9K9/280DmlwSV5Bd4P9TbruAx8bSzPpaf1U2T+ny8EprBkkERjHwLFgHsDzwkSSTwGb043/9i/ARdP7x9D10hyYg4kFLZ2+T3eO/EhVjWZsmyRPBx5Ld2P9AeC4qhpj0Wn0x4Q58D5qIsmvgbPozolnsfYENaMZZP3qsPCk0ehnIfg6XYuGYt0TxdzPZtbn4Nf96jozeY1kBq8rgHOA3/SbJnmY5GDuZyYBSPJJ1rz3ySxuE1VVYxg41jzgeWGiL0JOfxau3MVIZqoxB+ZgYuqa6QS6lg0Lr5nmvityn4Nv0HUxhHVzMJYZP89l5MeEOfA+aiLJO1nw3qdUVT19wHA2KHa105g8rXUArVXVRlfldUm2rqqfzTqeRnZsHcBKUFX3uyqvS7J3VX18xuE0Yx48L0xU1S2vyuuS3GFeB143B+ZgyitYc3N1g5aBNLRH6wBWAo8Jc9Ab/X0UQFU99aq8LslTquqYGYezQbHFk7RAkjdX1XNbx9FSkq9U1V1ax9FSks9X1T1ax9Gan4WOeTAHE+bBHIA5mEjy4qr6m9ZxtJTkX6rq0a3jaM1jwhyA91ETfhbWdZWeckojc6/WAawAWf9L5t6oBktchp+FjnkwBxPmwRyAOZjYr3UAK8Dcd7O6ijwmzAF4HzXhZ2EBC0+SFmNTSHMwYR465sEcTJgHcwDmYMKbKz8LE+bBHGgNPwsLWHiSJEmSdHV4cyVJ67Iov4CFJ2ldnijMAZiDiXNbB7BCnNs6gBXAY6JzaesAVgBzYA4mPC+YgwmPCXMAIz4ekmw3tfq5ZoGsUBaeNGpJtk6y8AT5xibBDCDJNsv9TL10r2ZBrhxPah3ALCV50dTyfgv2HTlZrqpHDRnXSpFk7yRXzmI3z3lIsufU8o4L9k2/77k+LyS5RZItp9b3SPLGJAcn2XSyvaru3ibC2TMH5uBqeH/rAFpIMj2OzSHNAhmAx4Q5+APN7X3UYpJsleTAJCcDX51sr6rnNAxrRXJWO41Gkr8GjquqbyVZBXwM2BW4HHh8VZ3UNMABJLkCOJ/uPcPaTyWqquZ+gMwk57B214BMrVdV7Tx8VMObnm1j4cwbY5qJoy+6/ANwU+BDwKuBd9B9Ll5VVR9sGN4g/Cx0kvwXsG9V/TDJbsBJwN8AuwCXVdUzmgY4AHNgDiaSPBP4ZFWd1T+g+yfg0XStP59aVV9pGd8QkmwMPBbYHvhYVX0jycOAlwCbVdWdmwY4EI8JcwCQ5F+X219VjxgqltaSbAY8Eng8cGdgc2Af4NNVdUXL2FayTVoHIA3occAr++Wn9P/dFrgNcAzdl8i8exOwB13zz/cCn63xVZ9XL1jfiO7C8gVMPakYgSyxvNj6PHsd8Czg88CD+/8eWlVvaRrVsPwsdDarqh/2y08E/qmqXpdkI+C0hnENyRyYg4nnAe/slw+gu8Heke4m643AvduENai3AzsAXwTelOSHdNcQh1bVh5pGNiyPCXMAcA/g+3T3D//FuK4PrpTkPXTnvxOBNwOnAGdX1SdbxrUhsKudxuTSqSLLA4Fjq+r3VfVNRlKEraqDgN3omsY/Cfhqktcs7F4zz6rqoqq6CPgZ8DDgE3Rfpg+tqkc3DW5YtcTyYuvzrKrqk1V1SX8j8YORFZ3Az8LE9EX0nsDJACN7emkOzMHE5VV1Wb/8MOBd/ffnScD1G8Y1pNXA3lX1YuAhdHm418iKTuAxAeYA4I/oWvvdka74vDdwYVV9qqo+1TSyYd2e7h7im8A3q+r3jOta6Wobxc221LskyR2BH9O1+nnB1L7rtQlpeH3x7RNJvgrsT9cK7CzgbU0DG0iS6wBPB54PfBbYp6rObhtVE7smuZjuYmqzfpl+/brtwhrcVgvGMdpken0MXe2Anfom9Jlapl8fTVEaOCXJccD/AFvTPcUkyU0Yz4Cx5sAcTFzRv+ef0Y3v9qqpfZu1CWlwl04KC1X1uyTf7R9cjc0nPCY8L/QFlo8BH+uHLDkA+GSSI8b0wK6qdktyO7r3f1KSC4HNk2xXVT9uHN6K5hhPGo0kd6drNr4t8IaqemW//SHAk6rqgIbhDSLJ9en6JD+OLg8fpBv36ntNAxtQkskYV28A1nnfIyk0qJfkHcvsrqp6+mDBNJLkvsvtH8uTzH4cm8cBN6E7L/6g335n4MZV9R8t4xuCOTAHE/1YRv8X2Bj4cFU9s99+X+BFVfXQlvENIclvgMmDqQA79+uh+37YpVVsQ/KYMAcTfcHpoXRFl1sC/0rX7fAHLeNqKcnudPl4LHB+Vd2zcUgrloUnaUSS/JquddOx/X/XOgGMoeiS5J0s3SR2FIUGgCTXoxsQ87J+/bZ0XQnOrarjmwanQSV5Z1U9tXUcrSW5XVV9q19eVVWXTO27e1V9oV10w+q7X9+hXz2zqr7bMp4h+TlYI8kmwOZV9bOpbdenu3/4VbvIhpHkFsvtr6rzhoqlpSQnVtUDWsehtpK8i66b3Ufphiv5RuOQVpS+OHnvqvp061hWKgtPGo0kBy/YVMCFdANsn9MgpMFZdNFEkk8DB/YzFt2KbvDUf6bru/6lqjq0aYADSfLkZXZXVb17sGAaGdPMdctxdj9IsgXwj8DuwNf6zbsBX6Y7X1y81O/OCz8HnQVdkGHNNdNpVfXLBiENrv9u3K6qPrdg+72AH1XVd9pENqwkXx3LDH5LSfJL1h0P8UK6cUIPGUMXzH5m7F/3q+vMDl1VWwwf1fCSHFdVj+2XX11Vh0zts0i7DMd40phsvsi2WwIvTXJ4VR07cDyDs1XDogXItVTV3w0VS2NbV9VZ/fJTgPdW1XOTbEp3kzmKwhNw1yW2P4JuCu25LzwB1+u7Cyw6Q02NYNr0nrP7dTOfngnsPxnbpn+K+3LgLcByhdp54eeg8/BFtm0D7JLkwKo6ZeiAGngD8OJFtl/c71ssR/Noy0UKkVcaQ2v5qlrnHiLJ1sBTgX8A9hs6pqFVlZOSdW49tbw3cMjU+rYDx7JBsfCk0aiqIxbbnmQb4CS67mdzzVZfwOIFyDGaflq1J3AUQFVd2j/VGoWqeu5kub/BfgLdRcQXWHsw3Xm2PfA6Fr+pLrrPxxg4u183Y9dTpzf0E1K8IslZi//K3PFzAFTV0xbb3nc/Ow74k2EjamK7qjp94caqOj3JLYcPp5kt6Wb0W+o7Yu4LT4vpu6C+PsmTWscyhP5+aVoBP6/xdZ9a7v2OLRd/EAtPGr2q+ml/wzkGtvpaogA5Ql9P8lrgB8CtgBMBkmzVNKoG+nFMnko30+UXgMdU1bebBjWss6tqLMWl5dwsyZvobq4my/Tr27cLa8UYy/ekn4NlVNV5/eywY7Dc9+FYZvYDOM+hGBbXHwtjuZ/+Ml1hZfq7YPMkpwHPqKpzm0Q1vEkr8Y3oZoWetBgP4zov/MHGcqBIS0qyB910wXPPVl+Q5K+X2V2T2Q5H4JnA8+gKjw+oqt/0228PvLZVUENL8my6PJwMPGhEF05XSZK7VtWXWscxkBdOLZ+6YN/C9Xn1n/058pXTT7GTvBz4fLuwBuXnYBn9RBSXrPeF8+HUJM+sqrdNb0zyDLqb8LEYS9F5SUt0Ndyabqa7DwwcThNVteNi2/vc/APwoGEjauZ/gMmwHD+aWp6sawkOLq7RSHI66zaB3Ab4IfDkySw2YzWWwSOT/NUim68PHAjcsKpuMHBITSW5Ll2LJ+havvyuZTxD67sV/gS4gMUHy5z76bKTPKCqTpxavz3d1MAH0DWjX90suAElObKqXtI6jpb6wcXfDtwFOK3fvBvwVbrBxX/RKraVIMlrq+oFreMYQpIPs/g1002AJ1bV3Bcik2wHHA9cyppC02pgU2DfqhrFTWaSXarq6/3yKGd6TPKOBZsKuAj4ZFV9pEFIK8qYJl7Q1WfhSaOxyLS4BVxUVb9e7PVj0rf6evnYutsk2ZyutcuBdGNWvK6qftI2qmH03cuOBJ4GfI+u0LID8A7gpVV1WcPwBuN02Z1+vJJJseky4BbA6jG1APPCGZLcvKq+l2RnutaPAGeOZfau9Unyvaq6ees4hpDkvgs2TW60z6qqSxuE1Ex/jXTHfvWMkQysfiVnetRyktyAbqzY3VrH0lKSvYEXVdXerWNZqexqp9GY3ED2FxB36DefQTcV6iisr9XX8BG10XctPJhuIOljgLv0g0SOyVF0Y37tNJkau2/t8Nr+53kNYxvMWApLy0nyeWALuq62j66qs5KcM6aiU2/jfpaipWb3++nA8bTwIbrz4XcAi03rGk2Xo6r6FECSHVlzzXT+mIpOfYvgP6NrFXw68PaqurxtVE040yOQ5MF0sxxOivJnAK+uqo+2i2o4S8wKvTXdLMBvGTicZpLsSde18KZ035mvpntoG8YzKc3VYuFJo5Fke7qZN37HmibT+yV5NV2T6R80C244D1uwPrpWX0mOAh4FHA3cqap+1TikVh4G3GZ6HJequjjJnwPfYiSFpyS/ZPFZSCZd7bYYOKQWfkw3aPJ2dFMBn8U4Z2a5Hd13w1IzN+00bDhNjOYmcimLzNx05S5GlJ++RfDbgd2Br/Wbd0vyZbpulxc3C244x9C1AP0M8GDgj4GDmkbUxuhnekzyTOB/AS9izVhvq4G/TXKzqjq6WXDDWThBUdGNafTExWZ/nGOvA55FN+7hg/v/HlpVoym+XV12tdNoJDkeOKGq3rlg+5PpnvI/sklgA1rmghoYxxP9fkyfS4DLWXxMnzEUGkjy31V1mz90n+ZTki3pCrIHALemm83pgVX1xaaBDWgs49wtJ8lPWGaSiar6ywHDaSLJOaw7c9OVlhpgd94keSdwLvCKqrqi3xbg5cCtqmruW0knOb2q7tQvbwJ8cYzdyqbOC6EbTHtyjgjw2KrarlVsQ0lyJvCnC6+Tk9yQrpvZH7eJTENbpLvpt6vqti1j2lDY4kljcvuq2nfhxqp6V5KXtgiogcWmQp0YxRP9qtqodQwrxJlJnlxV75remOSJdC2eNBJJHlVVH6RrKv6OJDcGHgu8vh/zZ4e2EWpAv2Vcs3WtYyyFpavgXlX11OkNfQvZVyQ5q01Ig7tyrMOquryru42SMz12jTXWeThbVReN5XOxxIQDV6qqRwwYTktbLZjl8DrT6/31lBZh4UljsmjBIclGwMYDx9KEF9S2+prybOCDSZ7O2rP1bAasU6DVXHsZXTdkAPoB9t8CvGV9g6/PmSVb+ozIRVV1TOsgWkqybIuWqvrKULGsYOO404Zdk0y6FAbYrF8fVQvp5c4JSV47ZCwNXZxk16r62vTGJLsCv2wU09DG8m+9Pp9i7aFLPgU8vF8upq6ntDa72mk0krweuAFw0GRMoyTXB14P/G4kXQgeCGxeVR9YsP3RwMVV9fE2kQ1nPd0oqqrmvtXXtH6QxMnAsWdW1ckt49HwnJWok+Tf6B7I/UVVfbd1PC0k+UJV3b11HC0lWW7CkRrL7K9JjqEbYP6V02MBJnk53fiAT2oWnFaMscz0mORPgX+maxk8/bDuKXRjHH22VWwtJNkWoKouaB3L0JL81YJNVwAX0nW5PKdBSBsMC08ajSTXAf4GeCpwHmumjz8GeMkYZmpJ8jlgn4VfFEluBHy4qu7RJrLhJLmFM5lBkrsCN6qqf1+w/cHAT6pq1N1txiTJb4CzF9tFd6O9y8AhNZNkH7rvifcAb6W7oATG0Royye4s35XC1j4j0c9y+nbgLsBp/ebdgK/SDS7+i1axaeVI8v2xdMdO8kfAXzD1sA74+6r6UbuohpXkMOC5dL1IQjde6pur6hVNAxtQn4OFtgEeCBxeVbaeXoKFJ41Oks3opsYF+E5V/aZlPENKcmpVrV5i39fHcINp645OklOApy0swvVdq94xlqf6giRnAA9Zav/YCrV914lPAz9jTRFmFK0hbe3TjXm23P6xjd+RZGfWTB9/ZlV9p2U8Gt56Znr8WlXdbMh41EaSg+lmcXvWpGVPkp3oHtJ8rKpe3zK+1vrj5CTvMZbmGE8ajST3WWTz6smggFX16WEjamKLJJtU1eXTG/vWYJs1imloYxmbYn02X6ygUFXn9S3gNB6Xjq24tJgkq+jGu3oM8ISq+rfGIQ2uqvZoHcMK8PAFyx+eWh/N+B1JJt2nLgO+tnB7VX2vRVxqYrmJaS5bZNvc6YvyS7XWqKraa8h4GnkSsHdVXTjZUFXf7SelOZFu6JLRqqqfZiwjzV9NFp40Ji9cZFsBu9B1uRvDAOMfBN6W5DlT41zdAHgjI7mYBrZP8qaldo5hrK/e1svsu95gUWgl+FzrAFaIrwP/Atylqn7bOpgW+huIVNW7F2x/EvD7qnpPm8iGU1VPmywn+er0+sh8hHWLDQVsC9yYcVwzCSem6b1gkW13B14E/GTgWFq5znTRaaKqLugfYI9akj3oWkprCRaeNBpVNf0UkyT3onu6/SO6/spj8DLgfwPnJZm0cLg53TgOL2sW1bBGP11476QkrwJeNhk4tn9ScwRwStPINLRzkhxYVW+f3pjkQLqWcW9oFNfQnglcuLDolOT2wAUjGUT1ucBiT+4/SNf9cO4LTwuMdjyKqrrT9HqSWwKHAPcHjmwQkhpJ8sSq+n/98r2q6nNT+55TVW9pF90wpse9THJf4OXAdYE/WzhW5hxbbizcuR8ndyLJ6az73bAN8EPgycNHtOFwjCeNTpK96L4wCjhyDDO5LbRgnKuzq+q3Sf6kqv6rZVxDcIynTj+j4z8Cd2PNwLG7AqcCz6iqX7WKTcNK8mXg7lV12YLtmwKnjmHsN4AkxwL/Z2G36yT3Bv68qh7fJrLhLHd+HMs4gNP8voAktwZeCvwJ8DrgmIXnCs236eNg4TExpmOknxn6ZcAlwKuqarkx8eZOkt8Dv2bdVpABrltVo2j11I+FOq2AiyY9SbQ0WzxpNJI8lO7i6Rd0rTxGNfXptP6J/ukLNr+frvXTvFv0qUw/Ve4BVfXsgeNpov+CPKAfGHIyQ8sZY51GfuQ2WexGsqouHdl4BbdabKy/qvpMkre2CKiBzZJcf+EFdJLNgU0bxTSoJB9mzc3UTkn+dXp/VT2iSWADS3JHumumOwCvoZvJ7vdto1IjWWJ5sfW5lORLdN1MjwI+32+7suA2hhk/q8rutYxvwpVrk4UnjcmHgfOBi4AXJXnR9M6xXEwuYxQXD1V198lykjsDjwf2A85hPONcrXXBBPyA7kbLp9jjtFGS7arqx9Mbk2zXKqBGNl9m3yie5NJ1u/5Akj+bXFz3Xaz+vt83Bq9dYnlsvgZ8n26sp7sBd5uuQ49oPESt3a1oYVeZsXSd+TXwK7rJJx6zYF8Bcz/j50Q/ltHkgeU3quqTDcPRBsTCk8bE2XqWN4qLhyS3AQ7ofy4E3kfX7Xhsn4/XLbJtm7571QFVddoi+zWfjgI+kuSvgMlT29377WO68T47yUOq6qPTG5M8GBhFS8Cqem2SXwGf7ieeCPBL4G+rahStvqrqU0l2o+uOfkZVfbN1TI0cyEiuC7Ret0vydbrzwc79Mv36Tu3CGk5V3a91DK0l2Z7uAe3vWDNW6n798B37VtUPmgWnDYJjPGk0kmxRVRcvse/mY5gaeKoLwTq7gD2r6voDhzS4JFcAn6HrNnB2v+27VTWKi6f1SbIa+Luquk/rWDQza8MzAAAWhUlEQVScvrhyKHBHunPEGXTFhrEMmjoZy+YjwH+y5qJ6NXAP4GFV9d+tYmuh715HVf2ydSxDSvLXwBPpPgN/AvxNVb2tbVQrS5JNqury1nFoGIuMabOWMXQ9SvKiqnpNv7xfVb1/at+RVfWSdtENI8nxwAlV9c4F258MPLqqHtkkMG0wLDxpNBYMjnhyVe212L551s/EsaSq+tRQsbSSZB9gf+BewMeAY4F/dLrgNcZyPGh5Sa4LPHz6AnveJVlF1/32jv2mM4D3VNXv2kU1rH5snxcyNfYb8NqqWjgu4FxKcgZw16r6TZIbAh+rqru2jmtoST5bVX/aL7+7qp40tc/viBFJcmJVPaB1HC05wDok+XZV3fYP3SdN2NVOYzI9htE2y+ybW0sVlpLsQFeMmfvCU1V9CPhQP6vbI4GDgBv3gwcfX1UnNg2wsX5cH59IjFSSjYEH0nVFfQBd68DRFJ6q6pIknwQu6DedObKi0yPpulf+DWu6464GPpjkBVV1QrPghnNJVf0GoKouSrJR64AamW4BfYcF+0ZxzaQrbds6gBVg9AOsA4ueC/tzpAOPa70sPGlMHBxxSpJt6QbVPgC4KXB824iG1c/a9B7gPUm2psvFIcAoCk9J3sy6n/ttgHsCzxs+IrXUt4Z8PPAQ4It0LQJ3nNyAj0GSLYB/pBvf6jS6m4ndknyZrmvuol2158wrgL2r6typbV9PcgpwQv8z76ZnspuMaXPlzHYjmohkueui0V0zjdyWSR611M6qGsPELN5DdGNBvg04aDLzaf8Q9/XAR5f9TQkLTxqXGyc5mO5CcrJMvz6Kpzn9mB2PorvBvA3dIIE7VtXNmgY2oCR7VtUp/fKOVXVOVf0MODrJRY3DG9KpC9aLbsbHgwG7HY5IkvOB7wFvBV5QVb9Mcs6Yik69NwFnAvtX1RUA6abxejnwFuDJDWMbyiYLik4AVNW5ScYys9/CcUrGNMD+tK2S7EvXymGrqcJDgC3bhaUGtgQexuIte4pxzAi8a5KL6XKwWb9Mv37ddmEN6oXAkcB5SSbjet0cOAaY+zGudM05xpNGI8lhy+2vqiOGiqWVJL+la83wMuCzVVVjG1jbfvrrl+R7VXXz1nFoGEneAOwDfIOuFeAJwOljOi8AJDmrqm79h+6bJ0m+Rjeu1/cWbL8F8OGq2qVNZMPrxzi7Vb969pi6XAIkecdy+6vqaUPFora8NhJAkrsC5wM/pzs33g94OPAt4PCq+mm76LQhsMWTxuSjVfWl1kE09mK6sZz+D/DeJO9rHE8L9tNfP/MwIlV1UJLn011EHgC8hq5rxWPpzpu/ahnfCjGWY+Iw4KQkR7L2zH6H0nVFnntJNqF7qv904Dy6f/sd+kLMS6vqspbxDejFVfWj1kFoRVj0/DemCSiSfITuwcyHJt3MRuj/Avevqt/2Q1S8GHgusBtwNPCYlsFp5RvrgIkap6OTnJXklUlu3zqYFqrqDVV1d9Z0JfgQcNMkL0pym4ahDcl++utnHkamOp+oqmfRdbU8gO48cW7TwIb1n0n+uu9ed6UkLwc+3yimQfWTL+wH7Am8s//ZA3hsv28MjqIb727Hqtq9b+mxM7AV4+p2d1qSk5IcmGSr1sGoqekZDTdO8pAk76YrzD6uXViDOpquu+G5SY5Lsm+STVsHNbCNp1o1PQ44uqr+papezprWodKS7GqnUUlyW7oWP48DLgPeCxy72JgW8yjJrYDtqupzU9vuBLwRuG9Vzf2sFEl+Dnya7gnevftl+vU/raqtW8U2pCQfZvECU4A9q+r6i+zTyCR5cVX9Tes4htAPLv524C50g4tD9yT3q3SDi/+iVWxDSbJJVV3eOo6WkpwF3KYWXCD3Mz5+awxdLuHK93t/umumhwBfoLtmOqGqftsyNg1viQkodhrbWIBJrkfXvWx/4B7AvwPvqaqPNw1sAEm+AexWVZcn+RbwrKr69GRfVd2xbYRa6Sw8abSS7Er3xfFY4EdVda/GIc1ckn+jaz5/+oLtuwBHVtXD2kQ2nP7iaUlV9amhYmlpiTxMvhAyljxoeWMa7yvJrlX1tSQ7A5NWsWdW1XeS/HlVvbVlfENYMAbem6vqua1jGlqS/66qRVsAL7dvnvUtOx5Md820B3ByVT2hbVQayoIJKD40NQHFqCci6a+djwF2GcmD25fSFR4vpBtU/C79WLG3Ao4Zw32UrhnHeNIoJdkIuDGwHXB94CdtIxrMdguLTgBV9fV+8Ni5t1xBJcmYvjS3Am5WVX8PkOSLdLM7FiMZy0VXyVjGNgI4Psl+VfVl4DuTjUkOBx5Bd9M176b/vcd0Ppx2ZpInV9W7pjcmeSLdILqjU1WXJjkT+CawO/DHjUPSsD5ANwHF44DfJzmBkXbJT7Id3QPr/YGbAMcBT20Z01Cq6lVJTqZ73ydOtQrdiG6sJ2lZtnjSqCS5N93YJfsApwPHAh8cQxcKWO+sTWdX1dz30e67DzwW2B74WFV9I8nD6KaC3ayq7tw0wIEk+RzdtPHf79dPA/aiK8S+o6r2ahmfVoaRtXjaHXg/8ISq+nw/1tNbgdsCj6yqi5f9A3NguVk/xyLJ9nTTw/+WtQdY3wzYt6p+0Cq2oSXZge4G+wC674bJ8ASjLMCNWX8+vB/dZ+EhwJbAgYxkAookz6R777cF/oXuOPjPtlFJGxYLTxqNJN+nGwjxWOC4qhpLK6crJXkvcEpVvW3B9mcAe1fV3A8SmeSdwA50YxT8CfBD+lmbRjR4Lkm+VFV3nVp/S1U9p1/+Qj8IvUYgyS9ZeryvzapqNK2j+64TxwPPBp7Zb358VV3SLqrhJPkNcDbdv/3O/TL9elXVLq1iG1qSPYE79KtnVtXJLeMZWpL/pHtAcxzdTfaX1/MrGokk1wEeSFeIeWBV3ahxSDOX5J/oCq8nV9UVy7zuDlV1xnCRSRsOC08ajSS3qKrzrsLr5nZci76J8PHApaz9JHdTuie5cz91cj844i5VdUU/FfCPgJ2r6qLGoQ1quRZuSb5TVTsPHZPUUpJt+sXb0834eRLwHOAKgKnZfObW+rpcX5Xv0HnVz+z27Kp6VetYhpDkPsBnFg6yvsjrRjMBgdblv//axtpSVLoqLDxJC4zhSyPJHsBk9okzquqUlvEMaeG/7xj+vReT5J+BTy7S+u1/AferqgPaRCa1keQcupZf0+McTdarqnZqEpgG1XcvezlwU7oC5HuBVwBPppu96nkNw1txxvodqs6YumNfFUm+OpYhG6Q/1Giaz0tao6o+AXyidRyN3C7J1/vlADtPrTOiriTPBz6U5PHAV/ptuwOr6MZAk0Zl7DM0wVrFtys3Ta3XSFpCvgv4FN04Lg8CTgVOA+40hlbBV8OYJiDQuvz3X5stOqQlWHiSNDa70s1m+P0F23eg63Y3Cv0YZ/dcMI7JR8bU+k2almRhq40CLpwMwD8Sqxesb0Q3GcMLgK8OH04T21TV4f3yfyTZj27A+SXHdRk5b7THzX9/SVeJhSdpXT69mW+vB168cKySJFv0+x7eJKpG+kKTxSYJXrfItm2SbAocUFWnDR3Q0CZj3SXZCHgS8EK61j4PraozW8Y2pCRbs+Za4CJgy35Wr1GM9fUH8pppzk1NQDH5t54Um0I326PWuLR1ANJKZeFJo9EPJL15VV2wYPu2wC+r6nf9pjcOHpyGtF1Vnb5wY1WdnuSWw4cjaSWoqj0W255kNfAm4D7DRjS8fraqp9N1xf0ssE9Vnb38b82dLekm35guqEy6IxfgWF9re3/rADRbVbV56xhWiiTXqarLFmy7UVVdCOCMwNLSHFxco5HkaOBjVfXBBdv3BR5QVX/eJjINKclZVXXrJfYtOdObpPEaywDKSc4HLgfeAHxv4f6F35/z6KrOgDsGSR4I3IxuCvlzp7Y/var+qVlgGlT/4PbPgFsBXwf+qaoubxvVsPpJed4NXJeuEP2syTExlu8H6ZraqHUA0oB2X+yiuaqOZwRPsnWlU5M8c+HGJM+ge8otSVdKsh3jGcfkJLqJJ3al63Y8/fOwhnEN6fjWAawESY4EXgrcCTg5yXOndj+nTVRq5Bi68d9OBx7C4t2S591rgAdW1Y2Ao4GPJ5m0brK7qXQV2NVOY3K9ZfZZhB2Pg4DjkzyBNYWm1cCmwL7NopLUVJI3s26BaRvgnsDzho9oeFX11NYxrADeRHYeDty5qi5PcjjwniQ7VdXzMUdjc/uquhNAkrcDX2wcTwubVtUZAFX1gSTfBD6Y5BDG82BCukYsPGlMfpLkblW11hdmkrsCFyzxO5ozVfVjutnc9gDu2G92NjdJpy5YL7qBpQ/uZ4Gce0kOXm5/Vf3dULE0tH2SNy21s6r+cshgGtpk0p2qqn6e5OHA0UneT/egRuNx5ZhGfSGyZSytXJbkj6rqRwBVdUaSvYB/A3ZuG5q0YbDwpDF5IXBckneydkuXJwP7twpKbVTVJ+i6lEgSwPFVdfFiO5LcvKrWGfNoDi03iPBYnur/FrtdA3wnyX2r6lMAVfV74MAk/xt4dNvQNLBdk0zOjQE269cDVFVt0S60wRwKbAf8aLKhqs5Pcj/g2a2CkjYkDi6uUenH6vgL1rR0OQN4y1ieZkuSFjc9QGySk6tqr8X2jVWSg6rqDa3jmDX/rTtJNgOoqt8usm/7qvrB8FFJ7SXZBqCqfto6FmlDYosnjUrfzeqw1nFIklac6f4j2yyzb6wOppvtbt5d2jqAlaCqfptkyySPALbvN/8A+A+LThqbJDenG2B8L+Dn3aZsAZwCHDo966OkxVl40mgkOZ3FuwpMmgrvMnBIkqSVo5ZYXmx9jMZSfHt2kukWTwVcWFXfbxVQC0meTPeg7kS6ghPAHsCRSY6oqnc1C04a3vvoCu9P6LudkmRjYD/gWODuy/yuJOxqpxFJcovl9tf/b+9eY+yqyjCO/x9agQpUrBiUhEYJoihVKBCJAgk3AQMBMYYUFDCImGBCgQ8EJUKDXIJAGjREQIho5CIhoBWD3C+agLSFWIkiyqVUQrgVKtCAhdcPe48zDDNt6TBnz3D+v2Ry1l5rn3OeaZrMmXfevXbVE73KIkmaWJIsAy6gKbCc0I5pj+dW1ZZdZZsIkiytqpld5xhvSUba+28GzYbac6rqwR5H6kSSh4HPV9WLw+Y/CNxXVdt0k0zqvSSPVNUn3umapEF2PKlvjFRYSrIZ8HxZgZWkfncpg5trDx0D/Kz3cXovyX8YvTN4Wo/jdKKq9hhpPslOwIXA7r1N1Jkw8v+FN+mf7jdpwKIkFwFXAAPdj1sCRwIPdJZKmkQsPKlvJNkFOAd4ATgD+CWwGbBekiOq6qYu80mSulNV80ZbSzK3l1m6UlWru6tdX6uqhUk27jpHD50JLE5yM4O/aM8E9qH5DCX1kyOAo4F5DO55tgxYAFzWVShpMvFSO/WNJAuB7wEfAC4B9q+qe5N8CriqqnboNKAkaULql8vMNLr2rri/r6odu87SK+1ldfvy9s3Fl3eXSuq9JN+tqp90nUOazCw8qW8kebCqtm/Hf6uqbYesPWDhSZI0kiRP9vseT/0iyY95+yVmM4AvAMdX1YLep+q9JFsDm1fVn4bNfxF4uqr+1U0yqfeSLK6q2Ws+U9JovNRO/eTNIeOVw9aswEqSRuPPiP6xcNhxAc8DJ1bVMx3k6cp84JQR5le0awf2No4kaTKz40l9I8kbwCsMbpL66sASsGFVva+rbJKkbq1pY+2q8o91fSDJ9KpaMcrazKpa2utMXUhyf1XtPMrakqqa1etMUleSrGLw94a3LAFVVdN7HEmadPwQpb5RVVO6ziBJmpjcWFutO4HZAEluq6q9hqzdMLDWBzZdzVpf3OFQGmKJW3JIY7Ne1wEkSZKkCSJDxjNWs/ZetzDJMcMnk3wLWNRBHmlCSuIVE9JasONJkiRJatQo45GO38vmAtcnOZzBQtNOwPrAVzpLJXXj2qEHSQLsCRwGHABs3kUoaTJxjydJkiQJSLIMuICmu+mEdkx7PLff7m6YZA9gu/bwoaq6vcs8UpeS7EJTbDqYpiPyOOC3VbW802DSJGDhSZIkSQKSnLa69aqa16ssXUqyIfAdYGtgCXBZVa3qNpXUjSRnAV8DlgJXAdcDC6vq450GkyYRC0+SJEmS/i/JNcB/gXuA/YHHq2put6mkbiR5BvgHMB9YUFWvJXm0qrbqOJo0aVh4kiRJkoAkP1jNclXVGT0L06EkS6pqVjueCvy5qvrljn7SWySZAuwDzAH2Au4A9ga2tBNQWjtuLi5JkiQ1XhlhbiPgaOBDQF8Unmi6nQCoqlXNXspSf6qqN4CbgJuSbECzofg0YFmS26vqsE4DSpOAhSdJkiQJqKrzB8ZJNgGOB74JXA2cP9rz3oM+l2RFOw4wrT0OTefX9O6iSb2VZGfgyap6ur3MbiOaOzzeCNzZaThpkliv6wCSJEnSRJFkRpIfAn+h+SPt7Ko6uaqe6Thaz1TVlKqa3n5tUlVTh4wtOqnfXAy8DpBkd+Ac4ArgKeCgDnNJk4YdT5IkSRKQ5EfAIcAlwKyqernjSJK6N6WqXmjHhwKXVNV1wHVJHuwwlzRpuLm4JEmSBCR5E3gNWAUM/ZDsJWZSn0ryV2D7dr+zvwPfrqq7B9aqartuE0oTnx1PkiRJElBVbkMhabirgLuSPAesBO4BSLI18FKXwaTJwo4nSZIkSZJGkWQX4KPAzVX1Sju3DbBxVS3uNJw0CVh4kiRJkiRJ0riwnViSJEmSJEnjwsKTJEmSJEmSxoWFJ0mSpHWQ5GNJatjXi+v4WlskOT3Jwe92TkmSpC55VztJkqSxeQA4tx2/vo6vsQVwGnAFcMM7fXKSqVW1ah3fW5IkadzY8SRJkjQ2zwK3tl+3Jdk2yS1JViR5IskJAycmubedfzXJoiS7tUv3t49Htp1TRyX5eTveqX3uy0keb8dHtWvXJHkI+HWS9ZOcl+TfSV5Mcm2SD7fnH5vkySSvJVma5KQe/dtIkqQ+Z+FJkiRpbL5EU3x6FrgR+A3waZouqPuAC5Ic2J57C3AicDrwEeDydv777ePdwBzgrrV8732Bi4FfAKcAJwELgPnA/sBP2/POBZYDxwAXAXZHSZKknvBSO0mSpLG5Dzi1Ha8E/tiOzxhyzj5J7gBm0xSIpgwsJJkG3AycCTxWVVe382vz3pdX1YXt+QPFq2OHvm/7+AiwFbAnsAj41dq8uCRJ0lhZeJIkSRqb56rqVoAkn2nn/gCcN+Scp4GvA18GrqTZy+ksYEdgA6BGeN032sepSTYApo1wzlPDjlcBBwx57kB3+57AV2kKX2cDhwK7rsX3JkmSNCZeaidJkvTueZimu2hXYAfgk8BxNAWfAdOBzwKzhswtbx93SDInyWbA4+3cN4BzWPPntt/R/FHxSGAmsB+D3U/zgfcDi4GXaDYzlyRJGnd2PEmSJL1LqmpVkoNoCj2n0nQeLQaWAP+k6Trauz397oFxVT2a5ErgEJqOqN2AS4EDgcNp9nBauYa3PxvYiGaPqIOBx2j2fwLYFJgHbNLOnzzGb1WSJGmtpGqkzm5JkiRJkiRpbLzUTpIkSZIkSePCwpMkSZIkSZLGhYUnSZIkSZIkjQsLT5IkSZIkSRoXFp4kSZIkSZI0Liw8SZIkSZIkaVxYeJIkSZIkSdK4sPAkSZIkSZKkcfE/UPKSaRIPIhEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 662
},
"id": "YQnuIULnc_JM",
"outputId": "335fdcb9-7c11-48c0-cb3a-7f2c48a46a2b"
},
"source": [
"X_data = np.array(selected_final_data.drop(['ICU'], axis = 1))\n",
"Y_data = np.array(selected_final_data[['ICU']])\n",
"print(X_data.shape)\n",
"print(Y_data.shape)\n",
"from sklearn.decomposition import PCA \n",
"\n",
"labels = []\n",
"for i in Y_data:\n",
" if(i[0]==0):\n",
" labels.append(0)\n",
" else:\n",
" labels.append(1)\n",
"print(X_data)\n",
"Y_data = np.array(labels)\n",
"\n",
"#pca = PCA(0.80)\n",
"#X_data = pca.fit_transform(X_data)\n",
"print(\"pca \", X_data.shape)\n",
"model = TSNE(n_components = 2, random_state = 0) \n",
" \n",
"tsne_data = model.fit_transform(X_data) \n",
"\n",
"\n",
"# creating a new data frame which \n",
"# help us in ploting the result data \n",
"tsne_data = np.vstack((tsne_data.T, Y_data)).T \n",
"tsne_df = pd.DataFrame(data = tsne_data, \n",
" columns =(\"Dim_1\", \"Dim_2\",\"label\")) \n",
" \n",
"# Ploting the result of tsne \n",
"sns.FacetGrid(tsne_df, hue =\"label\", size = 6).map( \n",
" plt.scatter, 'Dim_1', 'Dim_2', s = 100).add_legend() \n",
" \n",
"plt.show() \n"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"(293, 42)\n",
"(293, 1)\n",
"[[1. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" ...\n",
" [1. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]]\n",
"pca (293, 42)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 483.875x432 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 249
},
"id": "Oq_e-QhR4NG6",
"outputId": "2448ef66-5a57-4b9d-d9df-ee8dab243a98"
},
"source": [
"selected_final_data.head()\n"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE_ABOVE65</th>\n",
" <th>DISEASE GROUPING 2</th>\n",
" <th>DISEASE GROUPING 3</th>\n",
" <th>DISEASE GROUPING 4</th>\n",
" <th>HTN</th>\n",
" <th>BIC_VENOUS_MEAN</th>\n",
" <th>CALCIUM_MEAN</th>\n",
" <th>CREATININ_MEAN</th>\n",
" <th>GLUCOSE_MEAN</th>\n",
" <th>INR_MEAN</th>\n",
" <th>LACTATE_MEAN</th>\n",
" <th>LEUKOCYTES_MEAN</th>\n",
" <th>LINFOCITOS_MEAN</th>\n",
" <th>NEUTROPHILES_MEAN</th>\n",
" <th>PC02_VENOUS_MEAN</th>\n",
" <th>PCR_MEAN</th>\n",
" <th>PLATELETS_MEAN</th>\n",
" <th>SAT02_VENOUS_MEAN</th>\n",
" <th>SODIUM_MEAN</th>\n",
" <th>UREA_MEAN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
" <th>RESPIRATORY_RATE_MEAN</th>\n",
" <th>TEMPERATURE_MEAN</th>\n",
" <th>OXYGEN_SATURATION_MEAN</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
" <th>HEART_RATE_MIN</th>\n",
" <th>RESPIRATORY_RATE_MIN</th>\n",
" <th>TEMPERATURE_MIN</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
" <th>HEART_RATE_MAX</th>\n",
" <th>OXYGEN_SATURATION_MAX</th>\n",
" <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
" <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
" <th>HEART_RATE_DIFF</th>\n",
" <th>RESPIRATORY_RATE_DIFF</th>\n",
" <th>TEMPERATURE_DIFF</th>\n",
" <th>OXYGEN_SATURATION_DIFF</th>\n",
" <th>AGE_PERCENTIL_10th</th>\n",
" <th>AGE_PERCENTIL_20th</th>\n",
" <th>AGE_PERCENTIL_80th</th>\n",
" <th>AGE_PERCENTIL_90th</th>\n",
" <th>ICU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.183673</td>\n",
" <td>-0.868365</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.932246</td>\n",
" <td>1.000000</td>\n",
" <td>-0.835844</td>\n",
" <td>-0.914938</td>\n",
" <td>-0.868747</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.875236</td>\n",
" <td>-0.540721</td>\n",
" <td>0.345679</td>\n",
" <td>-0.028571</td>\n",
" <td>-0.836145</td>\n",
" <td>0.086420</td>\n",
" <td>-0.593220</td>\n",
" <td>-0.285714</td>\n",
" <td>0.736842</td>\n",
" <td>0.000000</td>\n",
" <td>-0.162393</td>\n",
" <td>-0.500000</td>\n",
" <td>0.208791</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.459459</td>\n",
" <td>-0.432836</td>\n",
" <td>0.736842</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.912243</td>\n",
" <td>-0.780261</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.382773</td>\n",
" <td>-0.908714</td>\n",
" <td>-0.412965</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.939887</td>\n",
" <td>-0.399199</td>\n",
" <td>0.345679</td>\n",
" <td>0.085714</td>\n",
" <td>-0.836145</td>\n",
" <td>-0.551183</td>\n",
" <td>-0.683145</td>\n",
" <td>0.357143</td>\n",
" <td>0.867507</td>\n",
" <td>-0.587500</td>\n",
" <td>-0.145299</td>\n",
" <td>-0.678571</td>\n",
" <td>0.604396</td>\n",
" <td>-0.504274</td>\n",
" <td>-0.627027</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-0.626087</td>\n",
" <td>-0.613497</td>\n",
" <td>-0.572519</td>\n",
" <td>-0.852941</td>\n",
" <td>-1.000000</td>\n",
" <td>-0.878788</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.326531</td>\n",
" <td>-0.968861</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.959849</td>\n",
" <td>-0.828421</td>\n",
" <td>-0.729239</td>\n",
" <td>-0.836100</td>\n",
" <td>-0.784714</td>\n",
" <td>-0.779141</td>\n",
" <td>-0.503592</td>\n",
" <td>-0.564753</td>\n",
" <td>0.580247</td>\n",
" <td>0.200000</td>\n",
" <td>-0.937349</td>\n",
" <td>-0.132620</td>\n",
" <td>-0.506215</td>\n",
" <td>-0.119762</td>\n",
" <td>0.652398</td>\n",
" <td>-0.329167</td>\n",
" <td>-0.435897</td>\n",
" <td>-0.547619</td>\n",
" <td>0.216117</td>\n",
" <td>-0.247863</td>\n",
" <td>-0.506306</td>\n",
" <td>-0.263682</td>\n",
" <td>0.754386</td>\n",
" <td>-0.692754</td>\n",
" <td>-0.730061</td>\n",
" <td>-0.582697</td>\n",
" <td>-0.784314</td>\n",
" <td>-0.682540</td>\n",
" <td>-0.723906</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.913659</td>\n",
" <td>-0.851024</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.702202</td>\n",
" <td>-0.641079</td>\n",
" <td>-0.812725</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.990926</td>\n",
" <td>-0.457944</td>\n",
" <td>0.345679</td>\n",
" <td>0.142857</td>\n",
" <td>-0.903614</td>\n",
" <td>0.236838</td>\n",
" <td>-0.539451</td>\n",
" <td>0.189142</td>\n",
" <td>0.852390</td>\n",
" <td>0.000000</td>\n",
" <td>0.048433</td>\n",
" <td>-0.500000</td>\n",
" <td>0.362637</td>\n",
" <td>-0.076923</td>\n",
" <td>-0.293694</td>\n",
" <td>0.019900</td>\n",
" <td>0.912281</td>\n",
" <td>-0.826087</td>\n",
" <td>-0.811861</td>\n",
" <td>-0.725191</td>\n",
" <td>-0.901961</td>\n",
" <td>-0.761905</td>\n",
" <td>-0.959596</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.317073</td>\n",
" <td>0.357143</td>\n",
" <td>-0.891012</td>\n",
" <td>-0.891993</td>\n",
" <td>-0.959849</td>\n",
" <td>1.000000</td>\n",
" <td>-0.706450</td>\n",
" <td>-0.340249</td>\n",
" <td>-0.846339</td>\n",
" <td>-0.754601</td>\n",
" <td>-0.997732</td>\n",
" <td>-0.292390</td>\n",
" <td>0.345679</td>\n",
" <td>0.085714</td>\n",
" <td>-0.884337</td>\n",
" <td>-0.078189</td>\n",
" <td>-0.495292</td>\n",
" <td>-0.137566</td>\n",
" <td>0.835283</td>\n",
" <td>-0.291667</td>\n",
" <td>-0.418803</td>\n",
" <td>-0.404762</td>\n",
" <td>0.252747</td>\n",
" <td>-0.282051</td>\n",
" <td>-0.553153</td>\n",
" <td>-0.482587</td>\n",
" <td>0.894737</td>\n",
" <td>-0.843478</td>\n",
" <td>-0.820041</td>\n",
" <td>-0.821883</td>\n",
" <td>-0.980392</td>\n",
" <td>-0.904762</td>\n",
" <td>-0.966330</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE_ABOVE65 DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 \\\n",
"0 1.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 \n",
"\n",
" HTN BIC_VENOUS_MEAN CALCIUM_MEAN CREATININ_MEAN GLUCOSE_MEAN \\\n",
"0 0.0 -0.317073 0.183673 -0.868365 -0.891993 \n",
"4 0.0 -0.317073 0.357143 -0.912243 -0.780261 \n",
"8 0.0 -0.317073 0.326531 -0.968861 -0.891993 \n",
"13 0.0 -0.317073 0.357143 -0.913659 -0.851024 \n",
"18 0.0 -0.317073 0.357143 -0.891012 -0.891993 \n",
"\n",
" INR_MEAN LACTATE_MEAN LEUKOCYTES_MEAN LINFOCITOS_MEAN \\\n",
"0 -0.932246 1.000000 -0.835844 -0.914938 \n",
"4 -0.959849 1.000000 -0.382773 -0.908714 \n",
"8 -0.959849 -0.828421 -0.729239 -0.836100 \n",
"13 -0.959849 1.000000 -0.702202 -0.641079 \n",
"18 -0.959849 1.000000 -0.706450 -0.340249 \n",
"\n",
" NEUTROPHILES_MEAN PC02_VENOUS_MEAN PCR_MEAN PLATELETS_MEAN \\\n",
"0 -0.868747 -0.754601 -0.875236 -0.540721 \n",
"4 -0.412965 -0.754601 -0.939887 -0.399199 \n",
"8 -0.784714 -0.779141 -0.503592 -0.564753 \n",
"13 -0.812725 -0.754601 -0.990926 -0.457944 \n",
"18 -0.846339 -0.754601 -0.997732 -0.292390 \n",
"\n",
" SAT02_VENOUS_MEAN SODIUM_MEAN UREA_MEAN BLOODPRESSURE_DIASTOLIC_MEAN \\\n",
"0 0.345679 -0.028571 -0.836145 0.086420 \n",
"4 0.345679 0.085714 -0.836145 -0.551183 \n",
"8 0.580247 0.200000 -0.937349 -0.132620 \n",
"13 0.345679 0.142857 -0.903614 0.236838 \n",
"18 0.345679 0.085714 -0.884337 -0.078189 \n",
"\n",
" RESPIRATORY_RATE_MEAN TEMPERATURE_MEAN OXYGEN_SATURATION_MEAN \\\n",
"0 -0.593220 -0.285714 0.736842 \n",
"4 -0.683145 0.357143 0.867507 \n",
"8 -0.506215 -0.119762 0.652398 \n",
"13 -0.539451 0.189142 0.852390 \n",
"18 -0.495292 -0.137566 0.835283 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_MIN HEART_RATE_MIN RESPIRATORY_RATE_MIN \\\n",
"0 0.000000 -0.162393 -0.500000 \n",
"4 -0.587500 -0.145299 -0.678571 \n",
"8 -0.329167 -0.435897 -0.547619 \n",
"13 0.000000 0.048433 -0.500000 \n",
"18 -0.291667 -0.418803 -0.404762 \n",
"\n",
" TEMPERATURE_MIN BLOODPRESSURE_DIASTOLIC_MAX BLOODPRESSURE_SISTOLIC_MAX \\\n",
"0 0.208791 -0.247863 -0.459459 \n",
"4 0.604396 -0.504274 -0.627027 \n",
"8 0.216117 -0.247863 -0.506306 \n",
"13 0.362637 -0.076923 -0.293694 \n",
"18 0.252747 -0.282051 -0.553153 \n",
"\n",
" HEART_RATE_MAX OXYGEN_SATURATION_MAX BLOODPRESSURE_DIASTOLIC_DIFF \\\n",
"0 -0.432836 0.736842 -1.000000 \n",
"4 0.000000 1.000000 -0.626087 \n",
"8 -0.263682 0.754386 -0.692754 \n",
"13 0.019900 0.912281 -0.826087 \n",
"18 -0.482587 0.894737 -0.843478 \n",
"\n",
" BLOODPRESSURE_SISTOLIC_DIFF HEART_RATE_DIFF RESPIRATORY_RATE_DIFF \\\n",
"0 -1.000000 -1.000000 -1.000000 \n",
"4 -0.613497 -0.572519 -0.852941 \n",
"8 -0.730061 -0.582697 -0.784314 \n",
"13 -0.811861 -0.725191 -0.901961 \n",
"18 -0.820041 -0.821883 -0.980392 \n",
"\n",
" TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF AGE_PERCENTIL_10th \\\n",
"0 -1.000000 -1.000000 0.0 \n",
"4 -1.000000 -0.878788 1.0 \n",
"8 -0.682540 -0.723906 0.0 \n",
"13 -0.761905 -0.959596 1.0 \n",
"18 -0.904762 -0.966330 1.0 \n",
"\n",
" AGE_PERCENTIL_20th AGE_PERCENTIL_80th AGE_PERCENTIL_90th ICU \n",
"0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 1.0 \n",
"8 0.0 0.0 0.0 0.0 \n",
"13 0.0 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 0.0 "
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uex2Now7pBWj",
"outputId": "003441ac-7d3a-4a54-f59a-e7f6b93565d1"
},
"source": [
"print(X_data)\n",
"print(Y_data)"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"[[1. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" ...\n",
" [1. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]\n",
" [0. 0. 0. ... 0. 0. 0.]]\n",
"[1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0\n",
" 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 1 0 0 1\n",
" 1 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 0\n",
" 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 0 1 1 1 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1\n",
" 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0\n",
" 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0\n",
" 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uW54PL8Sn97v"
},
"source": [
"## Training and Testing using various classifiers\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NuEikuB2o40d"
},
"source": [
"Importing Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bBVDjTrZo3PH"
},
"source": [
"from sklearn.linear_model import LogisticRegressionCV\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.datasets import make_classification\n",
"from sklearn import svm\n",
"from sklearn import tree\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"import matplotlib.pyplot as plt \n",
"from sklearn.metrics import log_loss\n",
"from sklearn import tree\n",
"import graphviz\n",
"from sklearn.neural_network import MLPClassifier"
],
"execution_count": 32,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zy5QU6xOna4B"
},
"source": [
"Shape of Datasets"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pdBhgfi5nhKt",
"outputId": "1a0aae21-1767-4eac-eb9a-e91de4c77eb0"
},
"source": [
"print(X_data.shape)\n",
"print(Y_data.shape)"
],
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": [
"(293, 42)\n",
"(293,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LNNOo8in0ysg"
},
"source": [
"def ass(y_true,y_pred):\n",
" tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
" accuracy=(tp+tn)/(tp+fp+fn+tn)\n",
" specificity = tn/(tn+fp)\n",
" sensitivity=tp/(tp+fn)\n",
" print(\"Accuracy:\",accuracy*100)\n",
" print(\"Sensitivity:\",sensitivity*100)\n",
" print(\"Specificity:\",specificity*100)\n",
" print(\"ROC_AUC_Score:\",roc_auc_score(y_true, y_pred)*100)\n",
" "
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "y_HSSUxxpEiW"
},
"source": [
"Splitting Data into Training Data and Testing Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KiaJXUCGpL6F"
},
"source": [
"X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size=0.30, random_state=1)"
],
"execution_count": 35,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hPgM9dpWpSiB"
},
"source": [
"Performing Logistic Regression with Cross Validation Estimator"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GDgUYgh4plpD",
"outputId": "460421a1-1ae8-476e-b76f-70f9bcec0750"
},
"source": [
"lgc=make_pipeline(LogisticRegressionCV(cv=5,random_state=1,max_iter=5000))\n",
"lgc.fit(X_train, Y_train)\n",
"y_pred=lgc.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 84.0909090909091\n",
"Sensitivity: 66.66666666666666\n",
"Specificity: 91.80327868852459\n",
"ROC_AUC_Score: 79.23497267759562\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tPmQIpDApxpZ"
},
"source": [
"Performing Gaussian Naive Bayes "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nXQjXc67r9Mx",
"outputId": "e4156eda-edc9-4c2a-d9cf-85a9cfc993d5"
},
"source": [
"gnb=make_pipeline(GaussianNB())\n",
"gnb.fit(X_train,Y_train)\n",
"y_pred=gnb.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 82.95454545454545\n",
"Sensitivity: 66.66666666666666\n",
"Specificity: 90.1639344262295\n",
"ROC_AUC_Score: 78.41530054644808\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L_fWzmKksG_t"
},
"source": [
"Finding Optimal Depth (SGD Classifier)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QNOVQYrBsJE_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "61697ced-140c-4110-8c4e-9e01250de686"
},
"source": [
"mx=-1\n",
"ri=-1\n",
"for i in range(1,10000):\n",
" sgd= make_pipeline(SGDClassifier(random_state=i))\n",
" sgd.fit(X_train,Y_train)\n",
" pmx=mx\n",
" mx=max(mx,sgd.score(X_test,Y_test))\n",
" if(pmx!=mx):\n",
" ri=i\n",
"print(ri)"
],
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"text": [
"285\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n4Wp94dHsJsI"
},
"source": [
"Performing SGD classifier with optimal Depth"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TV8VNF7FsKXJ",
"outputId": "5ebcbd97-d1c9-492a-b765-46190600d10e"
},
"source": [
"sgd= make_pipeline(SGDClassifier(random_state=ri))\n",
"sgd.fit(X_train,Y_train)\n",
"y_pred=sgd.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 88.63636363636364\n",
"Sensitivity: 74.07407407407408\n",
"Specificity: 95.08196721311475\n",
"ROC_AUC_Score: 84.5780206435944\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZZRydhZNElFR"
},
"source": [
"Performing SVM ( Supoort Vector Machine ) classification on the given data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7_n1uW_RsMJo",
"outputId": "fbf6bce0-3583-4011-a544-97f224727b0a"
},
"source": [
"SVM_object = make_pipeline(svm.SVC(kernel='linear'))\n",
"SVM_object.fit(X_train,Y_train)\n",
"y_pred=SVM_object.predict(X_test)\n",
"ass(Y_test,y_pred)\n"
],
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 85.22727272727273\n",
"Sensitivity: 66.66666666666666\n",
"Specificity: 93.44262295081968\n",
"ROC_AUC_Score: 80.05464480874316\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LMyfx9OAMy6x"
},
"source": [
"Performing Decision tree classification\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uKEcm7VNunvc",
"outputId": "e9e38da3-5f11-4060-f16e-aec37a62e889"
},
"source": [
"DT_object=tree.DecisionTreeClassifier(criterion='entropy',max_depth=4,max_leaf_nodes=10)\n",
"DT_object.fit(X_train,Y_train)\n",
"y_pred=DT_object.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 93.18181818181817\n",
"Sensitivity: 92.5925925925926\n",
"Specificity: 93.44262295081968\n",
"ROC_AUC_Score: 93.01760777170614\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f9uLCuzh0y65",
"outputId": "40fbdce6-d04f-4c3e-ada7-027aada71244"
},
"source": [
"from sklearn import tree\n",
"import graphviz\n",
"text_representation = tree.export_text(DT_object)\n",
"print(text_representation)\n"
],
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"text": [
"|--- feature_34 <= -0.91\n",
"| |--- feature_12 <= -0.77\n",
"| | |--- feature_16 <= -0.49\n",
"| | | |--- class: 1\n",
"| | |--- feature_16 > -0.49\n",
"| | | |--- class: 1\n",
"| |--- feature_12 > -0.77\n",
"| | |--- feature_8 <= -0.88\n",
"| | | |--- feature_17 <= -0.10\n",
"| | | | |--- class: 1\n",
"| | | |--- feature_17 > -0.10\n",
"| | | | |--- class: 0\n",
"| | |--- feature_8 > -0.88\n",
"| | | |--- class: 1\n",
"|--- feature_34 > -0.91\n",
"| |--- feature_12 <= -0.84\n",
"| | |--- feature_36 <= -0.90\n",
"| | | |--- class: 1\n",
"| | |--- feature_36 > -0.90\n",
"| | | |--- feature_21 <= -0.46\n",
"| | | | |--- class: 0\n",
"| | | |--- feature_21 > -0.46\n",
"| | | | |--- class: 1\n",
"| |--- feature_12 > -0.84\n",
"| | |--- feature_27 <= 0.07\n",
"| | | |--- class: 1\n",
"| | |--- feature_27 > 0.07\n",
"| | | |--- class: 0\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 777
},
"id": "o67ZSie01OnD",
"outputId": "c77774ed-d757-48f2-87cb-efde3fddea0a"
},
"source": [
"features=['AGE_ABOVE65', 'DISEASE GROUPING 2', 'DISEASE GROUPING 3',\n",
" 'DISEASE GROUPING 4', 'HTN', 'BIC_VENOUS_MEAN', 'CALCIUM_MEAN',\n",
" 'CREATININ_MEAN', 'GLUCOSE_MEAN', 'INR_MEAN', 'LACTATE_MEAN',\n",
" 'LEUKOCYTES_MEAN', 'LINFOCITOS_MEAN', 'NEUTROPHILES_MEAN',\n",
" 'PC02_VENOUS_MEAN', 'PCR_MEAN', 'PLATELETS_MEAN', 'SAT02_VENOUS_MEAN',\n",
" 'SODIUM_MEAN', 'UREA_MEAN', 'BLOODPRESSURE_DIASTOLIC_MEAN',\n",
" 'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
" 'BLOODPRESSURE_SISTOLIC_MIN', 'HEART_RATE_MIN', 'RESPIRATORY_RATE_MIN',\n",
" 'TEMPERATURE_MIN', 'BLOODPRESSURE_DIASTOLIC_MAX',\n",
" 'BLOODPRESSURE_SISTOLIC_MAX', 'HEART_RATE_MAX', 'OXYGEN_SATURATION_MAX',\n",
" 'BLOODPRESSURE_DIASTOLIC_DIFF', 'BLOODPRESSURE_SISTOLIC_DIFF',\n",
" 'HEART_RATE_DIFF', 'RESPIRATORY_RATE_DIFF', 'TEMPERATURE_DIFF',\n",
" 'OXYGEN_SATURATION_DIFF', 'AGE_PERCENTIL_10th', 'AGE_PERCENTIL_20th',\n",
" 'AGE_PERCENTIL_80th', 'AGE_PERCENTIL_90th']\n",
"classes=['Non-ICU','ICU']\n",
"dot_data = tree.export_graphviz(DT_object, out_file=None, \n",
" feature_names=features, \n",
" class_names=classes,\n",
" filled=True)\n",
"graph = graphviz.Source(dot_data, format=\"png\") \n",
"graph"
],
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<graphviz.files.Source at 0x7f07b7cbc0b8>"
],
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text-anchor=\"middle\" x=\"565.5\" y=\"-513.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.958</text>\n<text text-anchor=\"middle\" x=\"565.5\" y=\"-498.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 205</text>\n<text text-anchor=\"middle\" x=\"565.5\" y=\"-483.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [127, 78]</text>\n<text text-anchor=\"middle\" x=\"565.5\" y=\"-468.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<polygon fill=\"#99cdf2\" stroke=\"#000000\" points=\"530.5,-425 316.5,-425 316.5,-342 530.5,-342 530.5,-425\"/>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-409.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">LINFOCITOS_MEAN <= -0.774</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-394.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.912</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-379.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 104</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-364.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [34, 70]</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 0->1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0->1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M515.8353,-460.8796C504.6584,-451.513 492.7038,-441.4948 481.2288,-431.8784\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"483.2893,-429.0386 473.3767,-425.2981 478.7931,-434.4037 483.2893,-429.0386\"/>\n<text text-anchor=\"middle\" x=\"475.5713\" y=\"-446.5016\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node11\" class=\"node\">\n<title>2</title>\n<polygon fill=\"#e78c4a\" stroke=\"#000000\" points=\"872.5,-425 658.5,-425 658.5,-342 872.5,-342 872.5,-425\"/>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-409.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">LINFOCITOS_MEAN <= -0.841</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-394.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.399</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-379.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 101</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-364.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [93, 8]</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 0->2 -->\n<g id=\"edge10\" class=\"edge\">\n<title>0->2</title>\n<path fill=\"none\" stroke=\"#000000\" 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x=\"176.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [11, 54]</text>\n<text text-anchor=\"middle\" x=\"176.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 1->5 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1->5</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M337.1114,-341.8796C316.0526,-331.7339 293.4068,-320.8235 271.9532,-310.4876\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"273.3679,-307.2842 262.8398,-306.0969 270.3297,-313.5905 273.3679,-307.2842\"/>\n</g>\n<!-- 6 -->\n<g id=\"node6\" class=\"node\">\n<title>6</title>\n<polygon fill=\"#f7d9c3\" stroke=\"#000000\" points=\"522,-306 325,-306 325,-223 522,-223 522,-306\"/>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-290.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">GLUCOSE_MEAN <= -0.879</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-275.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.977</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-260.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 39</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [23, 16]</text>\n<text text-anchor=\"middle\" x=\"423.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 1->6 -->\n<g id=\"edge5\" class=\"edge\">\n<title>1->6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M423.5,-341.8796C423.5,-333.6838 423.5,-324.9891 423.5,-316.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"427.0001,-316.298 423.5,-306.2981 420.0001,-316.2981 427.0001,-316.298\"/>\n</g>\n<!-- 17 -->\n<g id=\"node4\" class=\"node\">\n<title>17</title>\n<polygon fill=\"#4ba6e7\" stroke=\"#000000\" points=\"103,-179.5 0,-179.5 0,-111.5 103,-111.5 103,-179.5\"/>\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-164.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.42</text>\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-149.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 47</text>\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [4, 43]</text>\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 5->17 -->\n<g id=\"edge3\" class=\"edge\">\n<title>5->17</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M132.7811,-222.8796C120.5375,-211.2237 107.2339,-198.5587 95.0029,-186.9148\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"97.2012,-184.1751 87.5451,-179.8149 92.3746,-189.245 97.2012,-184.1751\"/>\n</g>\n<!-- 18 -->\n<g id=\"node5\" class=\"node\">\n<title>18</title>\n<polygon fill=\"#b7dbf6\" stroke=\"#000000\" 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d=\"M404.2637,-222.8796C400.3092,-214.3236 396.1034,-205.2238 392.0175,-196.3833\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"395.191,-194.907 387.8184,-187.2981 388.8368,-197.8439 395.191,-194.907\"/>\n</g>\n<!-- 14 -->\n<g id=\"node10\" class=\"node\">\n<title>14</title>\n<polygon fill=\"#6ab6ec\" stroke=\"#000000\" points=\"615.5,-179.5 505.5,-179.5 505.5,-111.5 615.5,-111.5 615.5,-179.5\"/>\n<text text-anchor=\"middle\" x=\"560.5\" y=\"-164.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.722</text>\n<text text-anchor=\"middle\" x=\"560.5\" y=\"-149.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n<text text-anchor=\"middle\" x=\"560.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 8]</text>\n<text text-anchor=\"middle\" x=\"560.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 6->14 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font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 13->15 -->\n<g id=\"edge7\" class=\"edge\">\n<title>13->15</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M345.7859,-103.9815C341.0092,-95.2504 335.9595,-86.0202 331.1494,-77.2281\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"334.1153,-75.3568 326.2451,-68.2637 327.9742,-78.7165 334.1153,-75.3568\"/>\n</g>\n<!-- 16 -->\n<g id=\"node9\" class=\"node\">\n<title>16</title>\n<polygon fill=\"#eb9f68\" stroke=\"#000000\" points=\"486,-68 373,-68 373,0 486,0 486,-68\"/>\n<text text-anchor=\"middle\" x=\"429.5\" y=\"-52.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.706</text>\n<text text-anchor=\"middle\" x=\"429.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 26</text>\n<text text-anchor=\"middle\" x=\"429.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [21, 5]</text>\n<text text-anchor=\"middle\" x=\"429.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 13->16 -->\n<g id=\"edge8\" class=\"edge\">\n<title>13->16</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M391.2141,-103.9815C395.9908,-95.2504 401.0405,-86.0202 405.8506,-77.2281\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"409.0258,-78.7165 410.7549,-68.2637 402.8847,-75.3568 409.0258,-78.7165\"/>\n</g>\n<!-- 3 -->\n<g id=\"node12\" class=\"node\">\n<title>3</title>\n<polygon fill=\"#f6d1b7\" stroke=\"#000000\" points=\"877,-306 654,-306 654,-223 877,-223 877,-306\"/>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-290.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">TEMPERATURE_DIFF <= -0.905</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-275.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.964</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-260.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 18</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [11, 7]</text>\n<text text-anchor=\"middle\" x=\"765.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 2->3 -->\n<g id=\"edge11\" class=\"edge\">\n<title>2->3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M765.5,-341.8796C765.5,-333.6838 765.5,-324.9891 765.5,-316.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"769.0001,-316.298 765.5,-306.2981 762.0001,-316.2981 769.0001,-316.298\"/>\n</g>\n<!-- 4 -->\n<g id=\"node17\" class=\"node\">\n<title>4</title>\n<polygon fill=\"#e5833b\" stroke=\"#000000\" points=\"1187,-306 980,-306 980,-223 1187,-223 1187,-306\"/>\n<text text-anchor=\"middle\" x=\"1083.5\" y=\"-290.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">TEMPERATURE_MIN <= 0.07</text>\n<text text-anchor=\"middle\" x=\"1083.5\" y=\"-275.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.094</text>\n<text text-anchor=\"middle\" x=\"1083.5\" y=\"-260.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 83</text>\n<text text-anchor=\"middle\" x=\"1083.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [82, 1]</text>\n<text text-anchor=\"middle\" x=\"1083.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 2->4 -->\n<g id=\"edge16\" class=\"edge\">\n<title>2->4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M872.717,-343.3779C904.0909,-331.6374 938.4845,-318.7668 970.2219,-306.8902\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"971.8408,-310.0215 979.9798,-303.2387 969.3874,-303.4655 971.8408,-310.0215\"/>\n</g>\n<!-- 7 -->\n<g id=\"node13\" class=\"node\">\n<title>7</title>\n<polygon fill=\"#399de5\" stroke=\"#000000\" points=\"729,-179.5 634,-179.5 634,-111.5 729,-111.5 729,-179.5\"/>\n<text text-anchor=\"middle\" x=\"681.5\" y=\"-164.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"681.5\" y=\"-149.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n<text text-anchor=\"middle\" x=\"681.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 5]</text>\n<text text-anchor=\"middle\" x=\"681.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = ICU</text>\n</g>\n<!-- 3->7 -->\n<g id=\"edge12\" class=\"edge\">\n<title>3->7</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M736.1209,-222.8796C728.2037,-211.6636 719.6269,-199.5131 711.6675,-188.2372\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"714.3486,-185.9662 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82</text>\n<text text-anchor=\"middle\" x=\"1205.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [82, 0]</text>\n<text text-anchor=\"middle\" x=\"1205.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">class = Non-ICU</text>\n</g>\n<!-- 4->12 -->\n<g id=\"edge18\" class=\"edge\">\n<title>4->12</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1126.1697,-222.8796C1138.1194,-211.2237 1151.1037,-198.5587 1163.0411,-186.9148\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1165.6053,-189.303 1170.32,-179.8149 1160.7175,-184.292 1165.6053,-189.303\"/>\n</g>\n</g>\n</svg>\n"
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ffGvJcYENNba"
},
"source": [
"Performing K-Nearest Neighbour Classifier \n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vcx8Mi-evX6V",
"outputId": "69902a97-6c26-4966-ecc5-555809875632"
},
"source": [
"KNN_object=make_pipeline(KNeighborsClassifier(n_neighbors=25,p=1))\n",
"KNN_object.fit(X_train,Y_train)\n",
"y_pred=KNN_object.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 82.95454545454545\n",
"Sensitivity: 51.85185185185185\n",
"Specificity: 96.72131147540983\n",
"ROC_AUC_Score: 74.28658166363084\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nb1UkljD0soy"
},
"source": [
"Performing Random Forest Classifier"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SWODFEAs01a_",
"outputId": "ab827304-e6a8-4f54-cad5-60abe8ea94c3"
},
"source": [
"RF_object = RandomForestClassifier(criterion='gini',random_state=23,max_depth=6,bootstrap=True)\n",
"RF_object.fit(X_train,Y_train)\n",
"y_pred=RF_object.predict(X_test)\n",
"ass(Y_test,y_pred)"
],
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy: 93.18181818181817\n",
"Sensitivity: 88.88888888888889\n",
"Specificity: 95.08196721311475\n",
"ROC_AUC_Score: 91.98542805100182\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vn5S8JzzKWLO"
},
"source": [
"##Performing Grid Search on Various ML Algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4HbI6650KnFq"
},
"source": [
"Grid Search on Decision Tree"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MnUr-QtDJxFX",
"outputId": "6c3d7228-ba5f-41d3-8f8f-711aca8de6e9"
},
"source": [
"param_grid = {'criterion':['entropy','gini'],'max_depth':np.arange(1,30),'max_leaf_nodes':np.arange(3,20),'random_state':[1,2]}\n",
"GS_DT=GridSearchCV(DecisionTreeClassifier(), param_grid,cv=5)\n",
"GS_DT.fit(X_train,Y_train)\n",
"GS_DT.best_params_"
],
"execution_count": 46,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'criterion': 'entropy',\n",
" 'max_depth': 7,\n",
" 'max_leaf_nodes': 13,\n",
" 'random_state': 1}"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qDQTK32CJ0aH",
"outputId": "929acf6c-60ce-4f28-fe80-44509ea65187"
},
"source": [
"GS_DT.score(X_test,Y_test)"
],
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9318181818181818"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QC2lGuBSJ2bd"
},
"source": [
"dt_train_score=[]\n",
"dt_test_score=[]\n",
"for i in np.arange(1, 30):\n",
" param_grid = {'criterion':['entropy','gini'],'max_depth': [i],'max_leaf_nodes':np.arange(3,20),'random_state':[1,2]}\n",
" GS_DT=GridSearchCV(DecisionTreeClassifier(), param_grid,cv=5)\n",
" GS_DT.fit(X_train,Y_train)\n",
" y_train_pred=GS_DT.predict(X_train)\n",
" y_pred=GS_DT.predict(X_test)\n",
" dt_train_score.append(log_loss(Y_train,y_train_pred))\n",
" dt_test_score.append(log_loss(Y_test,y_pred))"
],
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"id": "r7ZskDF4J4W6",
"outputId": "794ecda5-50b6-4d84-8693-7b573caf686d"
},
"source": [
"plt.title(\"Decision Tree Classifier : Error vs Depth\")\n",
"plt.xlabel(\"Depth\")\n",
"plt.ylabel(\"Error\")\n",
"plt.plot(np.arange(1,30),dt_train_score,label=\"Training Error\")\n",
"plt.plot(np.arange(1,30),dt_test_score,label=\"Testing Error\")\n",
"plt.legend()\n",
"plt.plot()"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A04gAJ53K1qy"
},
"source": [
" Best kernel Performance using Grid Search"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iPgeWyJCJ6gp",
"outputId": "3a8cb97d-4b28-44fc-9161-8341a855a103"
},
"source": [
"param_grid = {'kernel':['linear','poly','sigmoid','rbf'],'gamma':['scale','auto'],'random_state':[1,2,3]}\n",
"GS_SVM=GridSearchCV(svm.SVC(), param_grid,cv=5)\n",
"GS_SVM.fit(X_train,Y_train)\n",
"GS_SVM.best_params_"
],
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'gamma': 'scale', 'kernel': 'linear', 'random_state': 1}"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FC3L0uKkJ9Dd",
"outputId": "3ac53273-b8f2-4c6c-b9a9-71f20909dbb6"
},
"source": [
"GS_SVM.score(X_test,Y_test)"
],
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8522727272727273"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6r_cOsQjJ-pZ"
},
"source": [
"dt_train_score=[]\n",
"dt_test_score=[]\n",
"for i in ['linear','poly','sigmoid','rbf']:\n",
" param_grid = {'kernel':[i],'gamma':['scale','auto'],'random_state':[1,2,3]}\n",
" GS_SVM=GridSearchCV(svm.SVC(), param_grid,cv=5)\n",
" GS_SVM.fit(X_train,Y_train)\n",
" y_train_pred=GS_SVM.predict(X_train)\n",
" y_pred=GS_SVM.predict(X_test)\n",
" dt_train_score.append(log_loss(Y_train,y_train_pred))\n",
" dt_test_score.append(log_loss(Y_test,y_pred))"
],
"execution_count": 52,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"id": "bauKzIcYKAKi",
"outputId": "391a911b-6331-4802-e159-bced05db65cf"
},
"source": [
"plt.title(\"SVM: Error vs kernel\")\n",
"plt.xlabel(\"Kernel\")\n",
"plt.ylabel(\"Error\")\n",
"plt.plot(['linear','poly','sigmoid','rbf'],dt_train_score,label=\"Training Error\")\n",
"plt.plot(['linear','poly','sigmoid','rbf'],dt_test_score,label=\"Testing Error\")\n",
"plt.legend()\n",
"plt.plot()"
],
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fAMztA6VLBIJ"
},
"source": [
"Grid Search on K nearest neighbour"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a_cjavdRKCAf",
"outputId": "3a5b2545-0b40-40ad-f99a-411e01b7fa49"
},
"source": [
"param_grid = {'n_neighbors':[10,15,20,25,30,35,40],'leaf_size':np.arange(3,20),'p':[1,2]}\n",
"GS_KNN=GridSearchCV(KNeighborsClassifier(), param_grid,cv=5)\n",
"GS_KNN.fit(X_train,Y_train)\n",
"GS_KNN.best_params_"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'leaf_size': 3, 'n_neighbors': 15, 'p': 1}"
]
},
"metadata": {
"tags": []
},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6fOc-C7IKDs5",
"outputId": "5c8e663f-85af-4000-976f-d54ecf537c13"
},
"source": [
"GS_KNN.score(X_test,Y_test)"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7954545454545454"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SPG1C5UDKFS6"
},
"source": [
"knn_train_score=[]\n",
"knn_test_score=[]\n",
"for i in [10,15,20,25,30,35,40]:\n",
" param_grid = {'n_neighbors': [i],'leaf_size':np.arange(3,20),'p':[1,2]}\n",
" GS_KNN=GridSearchCV(KNeighborsClassifier(), param_grid,cv=5)\n",
" GS_KNN.fit(X_train,Y_train)\n",
" y_train_pred=GS_KNN.predict(X_train)\n",
" y_pred=GS_KNN.predict(X_test)\n",
" knn_train_score.append(log_loss(Y_train,y_train_pred))\n",
" knn_test_score.append(log_loss(Y_test,y_pred))"
],
"execution_count": 56,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"id": "0wN4euytKG2-",
"outputId": "7e0a4acc-1798-4a8c-d719-bb1092dc5ac3"
},
"source": [
"plt.title(\"K-Neighbours Classifier: Error vs Number of Neighbors \")\n",
"plt.xlabel(\"Number of Neighbors\")\n",
"plt.ylabel(\"Error\")\n",
"plt.plot([10,15,20,25,30,35,40],knn_train_score,label=\"Training Error\")\n",
"plt.plot([10,15,20,25,30,35,40],knn_test_score,label=\"Testing Error\")\n",
"plt.legend()\n",
"plt.plot()"
],
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {
"tags": []
},
"execution_count": 57
},
{
"output_type": "display_data",
"data": {
"image/png": 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3ILtkyRIqVKhA7dq1GTJkyJ31ZsapU6coX748ffv2pUqVKqxdu/au5+Hh4bzxxhtUqVKFqlWrMm/evDvxNGnShI4dO1KpUiWb7DtNy4r4xGRmbjpN889XM2zhHgL9vPjuydosHdqEjtUL6ySQCS53+vbBb/s5cO6aTddZqXBO3n+0cpZfFxERwYYNG/D09OTatWusXbsWLy8vVqxYwdtvv83ChQv/9ZpDhw6xatUqYmNjKV++PM8///y/7sPfuXMn+/fvp3DhwjRq1Ij169cTFhbGwIEDWbNmDSVLlqRXr17pxrV27Vpq1Khx5/nChQvx9PTk6NGjzJgxg/r163Pq1Km7ni9cuJBdu3axe/duLl++TJ06dWjatCkAO3bsYN++ffo2US1b3UhI4sfNZ5i05gSXYm9Rq1guRj5eheblQvSNCllk10QgIq8AzwIKY+zdp5RS8Vbz+wOfYwykDTBeKTXZnjFlp27duuHp6QlATEwM/fr14+jRo4gIiYmJab7mkUcewdfXF19fX/Lnz8/FixcJDQ29a5m6devemVajRg1OnTpFYGAgpUqVuvNj3KtXLyZNmpTmNtIqGjp16hTFixenfv36d6ZZP1+3bh29evXC09OTAgUK0KxZM7Zu3UrOnDmpW7euTgJatomNT2TmptNMWXuSqLgEGpTKy1c9a9CglL6F/H7ZLRFYhm8cAlRSSt0UkfkYA1hPT7XoPKXUS7ba7v2cudtLQMD/x8d+7733aNGiBT///DOnTp2iefPmab7G1/f/Y3p7enqSlJR0X8s8aLxpPc/s6zTNHqJvJDBt/SmmrT/JtfgkmpcP4aUWZQgrkcfs0JyevesIvIAcIuIF+APn7Lw9hxUTE0ORIkUAmD59us3XX758eU6cOMGpU6cA7pTh20qTJk2YN28eycnJREZGsmbNGurWrWvTbWhaWi5fv8XoPw/RePQqxq48Sv1SefntpcZMf6quTgI2YrcrAqXUWREZA5wBbgLLlVLL01i0i4g0BY4AryilwlMvICIDgAEAxYoVs1fIdjVs2DD69evHyJEjeeSRR2y+/hw5cjBhwgTatm1LQEAAderUSXfZ1HUE7777boZ3Lj3++ONs3LiR6tWrIyJ89tlnFCxYkEOHDtnsPWiatYvX4vnunxP8uOU0t5JS6FCtMC+2KE2FgjnNDs3l2G3MYhHJDSwEegDRwE/AAqXULKtl8gLXlVK3RGQg0EMp9dC91hsWFqZSD0xz8OBBKlasaOu34HSuX79OYGAgSilefPFFypYtyyuvvGJqTPqz0bIq4uoNJv5znPlbI0hWisdrFuH55qUpHRJodmhOTUS2K6XSPOOzZ2VxK+CkUirSEsQioCFwJxEopaKslp8MfGbHeFze999/z4wZM0hISKBmzZoMHDjQ7JA0LdNOXo5jwqpj/LzzLCLQLawozzcrTdE8/maH5vLsmQjOAPVFxB+jaKglcNepvIgUUkqdtzztCBy0Yzwu75VXXjH9CkDTsurIxVi+WXWM33afw9vTgz71izOwWSkKBecwOzS3Yc86gs0isgDYASQBO4FJIvIhsE0ptRgYIiIdLfOvAP3tFY+maY5l39kYxv99jD/3X8Dfx5Pnmpbi2calCAnyzfjFmk3ZtR2BUup94P1Uk4dbzf8P8B97xqBpmmPZceYq4/8+xt+HLhHk58WQh8rwVKOS5A5Iv08uzb5crmWxpmmORynF5pNXGPf3UdYfiyK3vzdvPFyeJxsUJ6efHsHObDoRaJpmN0op1hy9zPi/j7L11FXyBfryTvuKPFGvGAG++ufHUehPwgYepBtqMDpu8/HxoWHDhgBMnDgRf39/+vbt+8CxNW/enPPnz5Mjh1HxVqZMGRYsWPDA69W0e1FKseLgJcb/fZTdETEUDvbjw06V6R5WFD9vT7PD01LRicAGMuqGOiOrV68mMDDwTiIYNGiQTeObPXv2PRuMpe4+OrPdSetup7XUklMUS/cZYwEcuhBLsTz+jOpclc61QvHx0p0dOyr9LbaT7du38+qrr3L9+nXy5cvH9OnTKVSoEF9//TUTJ07Ey8uLSpUqMWrUKCZOnIinpyezZs1i3LhxrFy58k4yad68OfXq1WPVqlVER0czZcoUmjRpwo0bN+jfvz/79u2jfPnynDt3jm+++SZTYxuA0U22n58fO3fupFGjRly5cuWu53379mXQoEHcuHGD0qVLM3XqVHLnzk3z5s2pUaPGnU7oXnvtNTvvSc0ZJCWnsHj3Ob5ZdYzjkXGUDgngyx7VebRaYbw8dQJwdK6XCJa+BRf22nadBatCu1GZXlwpxeDBg/n1118JCQlh3rx5vPPOO0ydOpVRo0Zx8uRJfH19iY6OJleuXAwaNOiuq4iVK1fetb6kpCS2bNnCkiVL+OCDD1ixYgUTJkwgd+7cHDhwgH379t3VZURqvXv3vlM01Lp1az7//HPg7m6y+/fvf9fzatWqMW7cOJo1a8bw4cP54IMP+OqrrwBISEggdetuzT0lJKWwcEcE364+zpkrN6hQMIhvnqhF2yoF9TgATsT1EoEDuHXrFvv27aN169YAJCcnU6hQIQCqVatG7969eeyxx3jssccytb7OnTsDULt27Tudyq1bt46hQ4cCUKVKFapVq5bu69MrGrLuJtv6eUxMDNHR0TRr1gyAfv360a1btzvL9ejRI1Nxa64rPjGZeVvDmfjPcc7HxFM9NJjhHcJoWTG/7graCbleIsjCmbu9KKWoXLkyGzdu/Ne8P/74gzVr1vDbb7/x8ccfs3dvxlcvt7udtmWX06C7ndayLu5WErM3n2bSmpNcvn6LOiVyM7pLNZqUzacTgBPThXd24OvrS2Rk5J1EkJiYyP79+0lJSSE8PJwWLVowevRoYmJiuH79OkFBQcTGxmZpG40aNWL+/PkAHDhwIFMJJbOCg4PJnTs3a9euBWDmzJl3rg4093QtPpHxfx+l8ei/+WTJISoUDGLegPr8NKghTfWIYE7P9a4IHICHhwcLFixgyJAhxMTEkJSUxMsvv0y5cuXo06cPMTExKKUYMmQIuXLl4tFHH6Vr1678+uuvjBs3LlPbeOGFF+jXrx+VKlWiQoUKVK5cmeDg4DSXta4jyJcvHytWrMhw/TNmzLhTWVyqVCmmTZuW+R2guYyrcQlMXX+S6RtOERufRMsK+XnxoTLUKpbb7NA0G7JbN9T2oruhNiQnJ5OYmIifnx/Hjx+nVatWHD58OMM2C9nNHT8bV3ApNp4pa08yc9NpbiQk065KQV5sUYYqRdI+2dAcn1ndUGt2dOPGDVq0aEFiYiJKKSZMmOBwSUBzTsv3X2DwnJ0kJqfQsXphXmhRhnIFgswOS0u4AT726ZJbJwInFRQUpG/h1Gzu0rV4hi3cQ9kCgYzrVYuS+fSNAQ4hMR4mNobqvaDZGzZfvctUFjtbEZc70J+Jc1FK8ebCPcQnJjO2Z02dBBzJxnFw5TiEZq7BaFa5RCLw8/MjKipK//A4EKUUUVFR+Pn5mR2Klknztoaz6nAkb7WtoIeFdCQxEbD2C6j4KJRuYZdNuETRUGhoKBEREURGRpodimbFz8+P0NBQs8PQMuFM1A0++v0AjcrkpW+DEmaHo1lb/i6oFGjzsd024RKJwNvbm5IlS5odhqY5peQUxes/7cbDQ/i8a3U8dNcQjuPkGtj/MzT/D+QubrfNuEQi0DTt/k1Zd4Itp67wRffqFM6lxwl2GMlJsPRNyFUMGg2166Z0ItA0N3b4Qixjlh3h4coFeLxmEbPD0axtnQyXDkCPWeBt3wTtEpXFmqZlXUJSCq/O30XOHF588nhV3U2EI7keCas+gVItoEIHu29OXxFompsa9/dR9p+7xqQna5M30NfscDRrKz+AxDho9xlkQ4LWVwSa5oZ2nLnKN6uO0a12KG0qFzQ7HM3a2e2wcxbUGwQh5bJlkzoRaJqbuZmQzGvzd1MoOAfDH61kdjiatZQUWDIMAvNDszezbbO6aEjT3MyopQc5eTmOH5+rR5Cft9nhaNZ2z4Gz2+CxieCXM9s2q68INM2NrDt6mRkbT/N0o5I0LJ3P7HA0a/ExsOJ9CK0L1bJ3FEB9RaBpbiLmZiJvLNhN6ZAAhrUtb3Y4WmqrR0PcZej9E3hk7zm6TgSa5iY+WLyfS7G3+PmFhvh5e2b8Ai37XDoEW76DWn2hcM1s37xd046IvCIi+0Vkn4jMERG/VPN9RWSeiBwTkc0iUsKe8Wiau1q69zyLdp5l8ENlqBaay+xwNGtKwdJh4BMALYebEoLdEoGIFAGGAGFKqSqAJ9Az1WLPAFeVUmWAL4HR9opH09zVpdh43v55L1WLBPNiizJmh6OldnAxnPwHWrwLAebU29i7IMoLyCEiXoA/cC7V/E7ADMvjBUBL0c0bNc1mlFK8vWgvcQnJfNmjOt6e+v4Qh5JwA5a9A/krQ9jTpoVht6NCKXUWGAOcAc4DMUqp5akWKwKEW5ZPAmKAvPaKSdPczU/bI1hx8BJvtq1Amfx6uEmHs/4riAmH9p+Bp3lVtvYsGsqNccZfEigMBIhIn/tc1wAR2SYi2/SYA5qWOeFXbvDhbweoXyoPTzUsYXY4WmpXT8G6r6BKFyjR2NRQ7Hmd2Ao4qZSKVEolAouAhqmWOQsUBbAUHwUDUalXpJSapJQKU0qFhYSE2DFkTXMNKZYxBgDGdNNjDDikZe+Ahye0/sjsSOyaCM4A9UXE31Lu3xI4mGqZxUA/y+OuwN9KjzepaQ9s6vqTbD55heGPViI0t7/Z4WipHVsJh36Hpq9DsPndf9uzjmAzRgXwDmCvZVuTRORDEeloWWwKkFdEjgGvAm/ZKx5NcxdHL8by2bLDtKpYgG619VChDicpwRhwJk8paPCS2dEAdm5QppR6H3g/1eThVvPjgW72jEFzbOFXbhDs701O3eeNTSQmp/DK/F0E+nrxaWc9xoBD2jwRoo7CE/PByzG6/9b3kmmmSElRfL/mBA/9dzUdx63jXPRNs0NyCeP/Psa+s9f45PGqhAQ5xo+MZiX2AvwzGso+DOUeNjuaO3Qi0LJdZOwtnpq+lY+XHKRB6XxEXU+gx6SNhF+5YXZoTm13eDTjVx2jc60itK2ixxhwSCtGQHICtP3U7EjuohOBlq3WHo2k3di1bDwRxUedKjPjqTrMfq4e124m0eO7jZy6HGd2iE4pPjGZV+bvIn+QL+8/WtnscLS0nNlsdDPd4CXIW9rsaO6iE4GWLRKSUvh06UGenLKF3P7eLH6pEU82KIGIUC00Fz8+V4/4pBS6f7eRY5eumx2u0xn95yFORMbxedfqBOfQ9S0OJyUZlr4BQYWhyWtmR/Mv7pMIUlLg6F9mR+GWTkfF0e27jXz3zwmeqFeMxS81pkLBuwfdqFw4mLkD6pOioOekjRy+EGtStM5nw7HLTFt/iv4NS9C4rB5jwCHt+AHO74Y2H4FvoNnR/Iv7JIKdP8DsrrBtqtmRuJVfd53lka/XcTLyOhN61+KTx6uSwyftLpDLFQhi3sD6eHoIPSdtZN/ZmGyO1vlci0/kjQV7KJUvgDfbVjA7HC0tN67Ayg+heCOjFbEDcp9EUKMPlG0Df7wGh/80OxqXF3cridfm72bo3F1UKBjEkqFNaF+1UIavKx0SyPyBDfD38eKJ7zexKzw6G6J1Xh/+doAL1+L5okeNdBOsZrJVn0B8NLQbDQ56O6/7JAJPL+g6DQpVhwVPQcR2syNyWfvOxtBh3DoW7YxgyENlmDugfpZatxbPG8C8gfXJ5e9Dn8mb2Xbqih2jdV7L9l9gwfYIXmhemhpF9RgDDunCXtg2BcKegYJVzY4mXe6TCMAom3tiPgTmhx+7Q9RxsyNyKUopJq89weMT1nMzIZkfn63Pq23K43UfXR+H5vZn/sAG5A/ype/ULWw8/q8uqNza5eu3eHvRXioXzsngh8qaHY6WFqVgyTDwywUt3jY7mntyr0QARhLovRBUilFnEHfZ7IhcwuXrt3h6+lZG/nGQZuXys2RoExqUfrAexQsG+zF3YH2K5MpB/2lbWHNE9zwL/x9jIPZWEl/2qIGPl/t9jZ3CvoVwZoMx6ph/HrOjuSf3PILylYEn5sG1c8aVQYJuyFnVChcAACAASURBVPQg1h+7TLuxa1l/PIoPO1Xm+761yRPgY5N15w/yY+6A+pQKCeTZGdtYefCiTdbrzBbtOMvyAxd5o015yhXQYww4pFvXYfm7RlF0rb5mR5Mh90wEAEXrQpcpcG4nLHgakpPMjsjpJCanMPrPQ/SZspmcfl788kIj+lraBthS3kBf5jxXjwqFghg0azt/7rtg0/U7k7PRNxmxeD91S+bh6cYlzQ5HS8/aMRB7HtqPMbqadnDumwgAKnaAdp/BkaWw5HWjTE/LlPArN+g2cSPfrj5Oj7Ci/Da4MZUK58z4hfcpl78Ps56tZ4y7++MOftudetRT15eSonjjp92kKMV/u1XHU48x4JiijsOG8VC9l3HC6QTMGxvNUdR9DmIijCHjchV1yFZ/jmbx7nO8s2gvCIx/oiYdqhXOlu3m9PPmh2fq8fT0rQydu5OEpBS6uFE3yzM2nmLD8ShGda5K0Tx6jAGH9edb4OUHrT4wO5JM04kAoOX7Rn3Byg8hZxGo3tPsiBzSjYQk3v91Pz9tj6BWsVyM7Vkz23+QAn29mPFUXZ77YRuvL9hNQnIKveoWy9YYzHDs0nVGLT3EQxXy06NOUbPD0dJz+E84uhzajISgAmZHk2k6EQB4eECnb+D6Bfj1RePOotIPmR2VQ9l/LobBc3Zy8nIcL7Uow9BWZfG+j9tCbSGHjyeT+4UxaNZ2/rNoL4nJKfRtUMKUWLJDUnIKr83fhb+PJ6O66DEGHFZivHE1kK8c1B1odjRZ4t51BNa8fKDHLMhXHub1hfN7zI7IISilmLb+JI9/s4G4W0nMfrYerz9c3rQkcJuftyffPVmb1pUKMPzX/Uxee8LUeOxpwurj7I6I4ePHq5I/yM/scLT0bPoGrp40WhB72eauueyiE4E1v2Do/RP45YTZ3SA63OyITHUlLoFnZ2zjg98O0KRsPpYObUrD0o7TqZmvlycTetfikaqFGPnHQb5ZdczskGxub0QMX688SqcahTPVRYdmkpizsGYMVOjglKUJOhGkFlwEei+AxJtGg7ObV82OyBQbjl2m7VdrWHv0MiMercTkfmE2axtgS96eHoztWYPHaxbh82WH+eKvIygXufvr9hgDeQN9+LBjFbPD0e7lr/eMRqoPf2J2JPdFJ4K0FKgEPWfDlRMwt7dR9ucmEpNT+HzZIXpP2Uygnxc/v9iQ/o1KOnS5tJenB2O6Vad7WChfrzzK6D8Pu0QyGLPsMMcuXTfGGPDXYww4rFPrjFbEjV6G3MXNjua+6Mri9JRsAo99CwufgV8GQZepRqWyCwu/coOhc3ey40w03cNCGdGxMv4+znGIeHoIozpXw8fLg4n/HOdWUjLDO1Ry6AR2L5tORDFl/UmerF+cpuVCzA5HS09yktGfUHAxaPyy2dHcN+f4lpulalfjttK/3jNuK334Y7Mjsps/9pznrUV7QMHXvWrSsXr2tA2wJQ8P4aNOVfDx9GTq+pMkJKXwUacqeDhZw6vY+ERe/2k3xfP485/2eowBh7ZtKlzaD91ngncOs6O5bzoRZKThYKPB2cbxRjJo8ILZEdnUjYQkPvztAHO3hlOjaC7G9cr+tgG2JCK816Eivt4efLv6OAlJKYzqUs2pWuGO/P0g56Jv8tOghk5zReaW4i7DqpFQshlUfNTsaB6IPsoyIgJtP4VrZ2HZ25CzMFR+zOyobOLAuWsMnrODE5fjeKF5aV5pXc7020JtQUQY9nB5fDw9GLvyKInJKYzpVv2+usPObisOXGTetnBeaF6a2sVzmx2Odi8rP4SEOKObGictgrxNJ4LM8PCELpPhh06waIDR4Kx4Q7Ojum9KKX7YeJqPlxwkVw5vZj1Tj0ZlHOe2UFsQEV5pXQ4fLw8+X3aYhOQUxvas6dCJLur6Ld5atIeKhXLycqtyZoej3cvZHcY4xPVfgPzOX3znuN8KR+OdA3rNhVzFYE4viDxsdkT35WpcAs/9sJ33F++nUem8LB3axOWSgLUXW5Th3UcqsmTvBZ6ftYNbSclmh5QmpRTv/LyPazeT+KJ7dT3GgCNLSYGlwyAgBJq/aXY0NqGPtqzwzwN9FoCnD8zqCrHO1R3yxuNRtBu7ln+OXOK9DpWY2r8OeQN9zQ7L7p5tUoqPOlVmxcGLDJy5nfhEx0sGv+w6y5/7L/Bqm3JULGS/Xlw1G9gzFyK2QqsRRiNUF6ATQVblLgG958ONKKPB2a1YsyPKUFJyCl8sP8wTkzfh7+PJzy804pnGjt02wNaebFCCUZ2r8s+RSJ6ZsZUbCY4z/sT5mJsM/3U/YcVz81yTUmaHo91LfAz89T6E1jG6mXYRdksEIlJeRHZZ/V0TkZdTLdNcRGKslhlur3hsqnBN6D4DLh6A+X0hOdHsiNIVcfUGPSZt4uu/j9GlVii/DW5MlSKucRaTVT3rFuO/3aqz8XgU/adu5fot85OBMcbAHpJTFP/trscYcHj/fAZxkUYFsQu1K7LbO1FKHVZK1VBK1QBqAzeAn9NYdO3t5ZRSH9orHpsr2xoeHQvH/4bFQxxyUJule8/TfuxaDl+IZWzPGozpVp0AX/e+P6BzrVDG9qzJ9jNXeXLKZmJumpvEZ20+zbpjl3nnkYoUzxtgaixaBiIPw+aJUOtJKFLL7GhsKrt+FVoCx5VSp7Npe9mj1pPGbaWrP4XgUHjoHbMjAuBmQjIf/n6AOVvOUL1oLsb1rEmxvM7bNsDWHq1eGG9PDwbP2UGfyZuZ+Uxdcvlnfz9KJyKv88mSgzQrF8ITbjCmglNTyqgg9gkwxi9xMdl1bdMTmJPOvAYisltElopI5bQWEJEBIrJNRLZFRkbaL8r70exNqPkkrPkMtk0zOxoOXbhGx/HrmLPlDIOalWbBoAY6CaShbZWCfPdkbQ5fjKXnpE1EXb+VrdtPSk7h1fm78fXy5LOu1dyqvsYpHfodTqyGFu9AgOvdZWf3RCAiPkBH4Kc0Zu8AiiulqgPjgF/SWodSapJSKkwpFRYS4mD9rohAhy+hTGv441VjhCITKKWYuek0ncav5+qNRGY+U5e32lVw6PvmzfZQhQJM6RfGqag4ek7axKVr2de54HdrTrArPJqPHqtCgZx6jAGHlnjTaEyavzKEPWN2NHaRHb8S7YAdSqmLqWcopa4ppa5bHi8BvEXE+dKtpzd0mw4Fq8GCp+Ds9mzdfPSNBAbO3M57v+yjfqm8/PlyE5qUdbCE6aCalA1hWv+6nI2+Sc9Jmzgfc9Pu29x3NoYv/zpCh2qFnLJPJ7ezfixEn4H2n4Gna9axZUci6EU6xUIiUlAs18QiUtcST1Q2xGR7voHGoDYBITC7u9GFdTbYfMJoG7Dq8CXefaQi0/rXIZ8btA2wpQal8zLzmbpExt6i+3cbCb9yw27bik9M5rX5u8kT4MNHnfQYAw7v6mlY9yVU7gwlGpsdjd3YNRGISADQGlhkNW2QiAyyPO0K7BOR3cDXQE/lzB3JB+aHPguNASpmdTE6pbKTpOQUvvzrCL2+34SvlweLnm/Es01KOV1Pm46idvE8zHq2HjE3Euk5aROnLsfZZTtf/nWEwxdjGd21GrkdcKAfLZXl74B4GIPRuzC7JgKlVJxSKq9SKsZq2kSl1ETL4/FKqcpKqepKqfpKqQ32jCdb5CtrdEVx7Rz82AMSbH92eS76Jk98v5mxK4/yeM1Qfh/ShKqh7tk2wJaqF83FnAH1uZGQRI9JGzl26bpN17/l5BUmrT3BE/WK0aJ8fpuuW7OD46vg4G/Q5DVj5EIXpmsS7aFYPaOTurPbjYFtUmzXpcGf+y7Qbuxa9p+L4aseNfhv9+oEunnbAFuqXDiYuQMakJyi6DlpE4cv2Kbl+PVbSbz20y6K5vbnnfYVbbJOzY6SE2Hpm5C7pNEVvYvTicBeKj5qtD48vASWvPHADc7iE5N595e9DJq1neJ5/fljSBMeq+naZylmKV8wiLkDGuDpAT0nbWTf2ZiMX5SBj/84SMTVm/y3u27U5xQ2fweXD0PbUeDl+nVuOhHYU70B0GgobJtiVDjdpyMXY+k4fh2zNp1hYNNSLBjUkBL5dCtUeyqTP5B5AxqQw9uTJ77fxO7w6Pte16pDl5iz5QwDmpaiTok8NoxSs4vYi7B6lHFLeLmHzY4mW2SYCETEQ0Sct/N9s7UcAVW6wsoPYPe8LL1UKcXszad5dNw6rsQlMOPpuvynfUXdRXE2KZEvgHkDGxDs702fyZvZfvpKltdxNS6BYQv3UL5AEK+21mMMOIUVIyAp3rgacJOGfhn+oiilUoBvsiEW1+ThAY9NgBJN4NcXjdaJmRBzI5EXZu/gnZ/3Ua9UXpYObUozPYh5tiuax5/5AxuQL8iXJ6dsYdOJzN/drJTi3V/2EX0jgS96VMfXy9OOkWo2Eb4Fdv8IDV6EfGXMjibbZPbUcqWIdBHdDv7+ePlCj1nGHUVz+8CFvfdcfOupK7Qbu4a/Dlzk7fYVmN6/DiFBrl9O6agKBedg3oD6FMmVg/7TtrD2aOa6OVm8+xx/7D3Py63KUbmwvqvL4aUkG/V5QYWg6RtmR5OtMpsIBmJ0EZFg6U46VkSu2TEu15Mjl9HgzDcIZneD6PB/LZKcohi74ig9vtuIt5cHC59vyICmpXXbAAeQP6cfcwbUp0TeAJ6ZsY1Vhy7dc/kLMfEM/3U/NYvlYmBTPcaAU9g5E87vgtYfGQ1E3UimEoFSKkgp5aGU8lZK5bQ818MoZVVwqDHCWUKcMajNzat3Zp2PuUmv7zfx5YojdKxemN8HN6Z60VwmBqulli/QlznP1ad8gSAGzNzGsv1pj1CnlGLYwj0kJKXwRfcaeOn+nhzfzavGYPTFGkLVrmZHk+0yfYSKSEcRGWP562DPoFxagcrQczZEHTeKiZJusXy/0TZg39kYvuhena961iTIz9vsSLU05A7wYdaz9ahSJJgXZu/gt93n/rXM7M1nWHMkkrfbV6CkvrvLOaz6xEgG7T9zmwpia5lKBCIyChgKHLD8DRWRT+0ZmEsr2RQe+xZOr2Pv+F4MnLmV0Nw5+GNIEzrXCjU7Oi0DwTm8mflMPWoXy83QuTtZtCPizrxTl+P4+I+DNCmbjz71i5sYpZZpF/fD1skQ9jQUrGp2NKbIbMuW9kANyx1EiMgMYCfwH3sF5uqOFmjLP379eTZ6Oj8WL0Dt577Rt4U6kUBfL6Y/XYdnZ2zjtZ92k5icQtfaRXl1/i68PEWPMeAslIIlw8AvlzHWgJvKyi+PdYG1vgXiPimlmLPlDI+OX8e3CY8QUfZJGlz8EZ9tk8wOTcsifx8vpvavQ9OyIby5cC/9p21hx5loPupUhULBOcwOT8uM/Yvg9Dpo+R74u29jv8xeEXwC7BSRVYAATYG37BaVi4q5mcjbi/byx97zNC6Tjy96VCd/QEuYfxX+fAtyFoJKncwOU8sCP29PJvWtzYuzd7Li4EXaVy1Ipxp6jAGnkBAHy9+DQtWhVj+zozFVholARDyAFKA+UMcy+U2lVNq3TGhp2n76CkPm7OLitXjealeBAdZdRneZDDM6wsLnILAAFKtvbrBalvh6efJtn1os3nWO1pUL6CIhZ7H2v8aY412ngYd7N/aTzHT/LyLblFJh2RBPhsLCwtS2bdvMDiPTklMU364+xpcrjlIkVw6+7lWTGmndFhoXBVPbGGMYPPMXhOjuCDTNbqKOw4T6xoAznb8zO5psISLb0/sdz2wdwQoReV1EiopIntt/NozRJV2IiafP5M2MWX6ER6oW4o8hjdNOAgABeaH3AmPYy1ldjI6vNE2zj2Vvg6cvtP7A7EgcQmbrCHpY/r9oNU0BuslkOlYcuMgbC3ZzKymFz7tWo2vt0IyLDPKUhCfmw/QORoOzp5YYLZE1TbOdI8vhyJ9GC+KggmZH4xAy1fso8JZSqmSqP50E0hCfmMyIxft59odtFM6Vg98GN6ZbWNHMlxsXqQXdphv3Ns/vZwyQoWmabSTdgj/fhLxlod6gjJd3E5ntfdS9emC6T8cuXefxCRuYvuEUTzcqyaIXGlI65D76LCnXBh79Co6vhN9efuBBbTRNs9j4DVw5Ae1GgZceM/q2zBYNrRCR14F5wJ1RvZVSWe+g3QUppZi/LZwRiw+Qw8eTqf3DeKhCgQdbaa2+EHMW/hlljJfa4m3bBKtp7uraOVgzBso/AmVamR2NQ9F1BA/oWrzRNuD3PedpWDovX/aoQYGcfrZZefO34FoE/DMachaB2u59r7OmPZDl70FKEjz8sdmROJxMJQKlVEl7B+KMdpy5ypA5OzkfE8+wtuUZ2LQ0nrbsMloEOnwFsRfg91eMftLLtbHd+jXNXZxaD/sWQNNhxk0Z2l3uWUcgIsOsHndLNe8TewXl6FJSFN+sOka3iRsB+GlQA15oXsa2SeA2T2/oNgMKVoGf+sHZHbbfhqa5suQkWDoMgotC41fMjsYhZVRZ3NPqceoO5traOBancPFaPE9O3cznyw7TrkpBlgxtQq1iue27Ud9AeOInCMgHP3aHKyftuz1NcyXbp8HFfdBmJPj4mx2NQ8ooEUg6j9N67vL+PnSRdmPXsuN0NJ91qca4XjXJmV3jBgQVgD6LjDLOWV2Mlsiapt1bXBT8PdLo+l3345WujOoIVDqP03rusm4lJTN66WGmrj9JxUI5GderJmXymzCUXb6y0Gsu/NAJ5vSAvov1GY5me8dXGUUpSbfMjuTBJcTBrVho554DzmRWRomgumVsYgFyWI1TLICNbo1xbMcjrzP4x50cOH+N/g1L8Fa7Cvh5m9hBVbH60Pl7mN8XFj0H3X9w+w6zNBtKuAGLBxs/msUamh2NbZRtDfkrmh2FQ7tnIlBK3fcvjIiUx2h3cFspYLhS6iurZQQYizHwzQ2gv1LKIWpDlVIs2B7B+4v34+vlweS+YbSq9IBtA2ylUkdoN9o4a1s6DNqP0Wc7mm2s/wpiwqH/H1CisdnRaNkks+0IskwpdRioASAinsBZ4OdUi7UDylr+6gHfWv6bKjY+kXd+3sfi3eeoXyoPX/WoScFgB7sAqjfQ+MJuGGe5G+JlsyPSnN3VU7DuK6jSRScBN2O3RJBKS+C4Uup0qumdgB+U0Rf2JhHJJSKFlFLnsymuf9kVHs3gOTs4Fx3P623K8by9bgu1hVYfGq0lV7xvNDir1i3j12haepa9YxQztv7I7Ei0bJZdiaAnMCeN6UWAcKvnEZZp2Z4IUlIUk9aeYMyywxTI6cf8gfWpXdzBe9r28IDHvoXrl+CX5yEwP5RqZnZUmjM6thIO/Q4thxtdmmhuxe6jpYuID9AR+OkB1jFARLaJyLbIyEjbBWdx6Vo8faduYdTSQzxc2Wgb4PBJ4DYvX+gxC/KWgXl94MI+syPSnE1SAix9E/KUggYvmR2NZgK7JwKMeoAdSqm0Rlo5CxS1eh5qmXYXpdQkpVSYUiosJCTEpsGtOnyJdmPXsu30FT7tXJXxT9QkOEc2tQ2wlRy5oM8C8AmA2d0gJsLsiDRnsuU7iDoKbUcZJxaa28mORNCLtIuFABYDfcVQH4jJrvqBW0nJjPz9AE9N20pIkC+/vdSYXnWLOe94s8GhxghnCddhVle4GW12RJoziL0Aq0dD2Yeh3MNmR6OZxK6JQEQCgNbAIqtpg0Tk9ogQS4ATwDHge+AFe8Zz28nLcXT5dgOT152kX4Pi/PJiI8oWcIGRwApWMYqJoo4ZxUSu0CBIs68VIyD5FrT91OxINBPZtbJYKRUH5E01baLVY8XdXVvb3cLtEbz36z58vDyY9GRt2lR2saHqSjWDxyYYjc1+eR46TzYqlTUttTObYfccoyO2vKXNjkYzUXbdNWS62PhEhv+6n593nqVeyTx81bMGhYJzmB2WfVTrDtfOGmd7OYtAG307oJZKSjIsfQOCCkOT182ORjOZ2ySC5fsv8uuus7zauhwvtnDgtgG20uhlo9J4w9dG/UG9gWZHpDmSHT/A+d3QZYrRu63m1twmEXSuVYSqocGUc4W6gMwQMTraunbeuDUwZ2Go+KjZUWmO4MYVWPkhFG9ktCLW3J7bFB6LiPskgds8PKHLZAgNg4XPGmXCmrbqE4iPNvqrcta75DSbcptE4LZ8/KHXPKOuYE4PuHzU7Ig0M13YC9umQNgzULCq2dFoDkInAncQkBf6LAQPL5jVGWLTatunuTylYMkw8MsFLd42OxrNgehE4C7ylIQn5kHcZfixG9y6bnZEWnbbtxDObDD6E/J3ki5UtGyhE4E7KVIbus0w+iP6qR8kJ5odkZZdbl2H5e9CoepQq6/Z0WgORicCd1OuDXT4Ao6tgN9fNooLNNe3dgzEnjcGMdIj2mmpuM3to5qV2v0h5iys+cwY1Kb5W2ZHpNlT1HHYMB6q94Kidc2ORnNAOhG4qxZvG62PV39qtDHQxQWu68+3wMsPWn1gdiSag9KJwF2JwKNjjd4nf3sZggoZg3xrruXIMji6HNqMhCAHGXNbczi6jsCdeXpD9xlQoDLM7wfndpodkWZLSbeMq4F85aCu7mJES59OBO7ONwh6/wT+eWF2d2MAc801bBwPV04YLYi9fMyORnNgOhFoEFTQaHCWnACzuhh90WjOLeYsrBkDFTpA6YfMjkZzcDoRaIaQctBrLkSHw489IPGm2RFpD+Kv90ClwMMfmx2J5gR0ItD+r3gD6PI9RGw1OqlLSTY7Iu1+nFpntCJuNBRylzA7Gs0J6ESg3a1SJ2MQ80O/GxWNusGZc0lOMvoTCi5qjEmhaZmgbx/V/q3+IIgJNyobg0ONM0vNOWybCpf2Q/cfjJ5nNS0TdCLQ0tb6I7h2Dv4abnRhXbWr2RFpGYm7DKtGQslmULGj2dFoTkQnAi1tHh7w+ES4fgl+HgSB+aFkU7Oj0u5l5YeQEGeMTKcHnNGyQNcRaOnz8oWesyBvGZjbGy7uNzsiLT1ndxjjENcdCPkrmB2N5mR0ItDuLUduo8GZTwDM7mbcn645lpQUWDoMAkKg+ZtmR6M5IZ0ItIzlKmokg/hrRjKIjzE7Is3anrnGLb+tRoBfsNnRaE5IJwItcwpWNYqJLh82iomSEsyOSAMjKf/1PoTWMbqZ1rT7oBOBlnmlmkOnCXBqLfz6glEkoZnrn88gLtKoIPbQX2ft/ui7hrSsqd7DGMdg5QfGbaWtdR/3pok8DJsnQq0noUgts6PRnJhOBFrWNX4FYiJg/VdGg7O6z5kdkftRCpa+aVTit3zf7Gg0J2fXa0kRySUiC0TkkIgcFJEGqeY3F5EYEdll+Rtuz3g0GxGB9p9D+faw5A04+LvZEbmfQ7/DiVXQ4h0IyGd2NJqTs3eh4ljgT6VUBaA6cDCNZdYqpWpY/j60czyarXh4QpcpUKQ2LHwGwreYHZH7SLwJy96G/JUh7Bmzo9FcgN0SgYgEA02BKQBKqQSlVLS9tqeZwMcfnphnjHn8Yw+4fMzsiNzD+rEQfcYYcMZTl+5qD86eVwQlgUhgmojsFJHJIhKQxnINRGS3iCwVkcpprUhEBojINhHZFhkZaceQtSwLyGcMaiMeMKuz0SWFZj9XT8O6L6Hy41CyidnRaC7CnonAC6gFfKuUqgnEAW+lWmYHUFwpVR0YB/yS1oqUUpOUUmFKqbCQkBA7hqzdlzyloPd84zbG2d3g1nWzI3Jdy98xkm6bkWZHorkQeyaCCCBCKbXZ8nwBRmK4Qyl1TSl13fJ4CeAtIrrmyxkVqQ1dp8GFPbDgKaNffM22jq+Cg79Bk1eNu7U0zUbslgiUUheAcBEpb5nUEjhgvYyIFBQxukkUkbqWeKLsFZNmZ+XbwiNfwNHl8McrelAbW0pONG4XzV0CGgw2OxrNxdi7pmkwMFtEfIATwFMiMghAKTUR6Ao8LyJJwE2gp1L618OphT1lNDhb8znkDNWdoNnK5u+M7j16zQVvP7Oj0VyMXROBUmoXEJZq8kSr+eOB8faMQTNBi3eMXkpXfwLBRaBmH7Mjcm6xF2H1KCjTGsq1NTsazQXpe8802xOBjl9D7HlYPASCCkKZVmZH5bxWjICkeGMsaT3gjGYHupcqzT48vaHHTChQCeb1hXO7zI7IOYVvgd0/QoMXIV8Zs6PRXJROBJr9+AZB7wXgn8e4rfTqabMjci4pyUYXHkGFoOkbZkejuTCdCDT7CipoNDhLToBZXeDGFbMjch47Z8L5XdD6I/ANNDsazYXpRKDZX0h5426X6DMwp6fRV452bzevGoPRF2sIVbuaHY3m4nQi0LJH8QbQeZJR5r3oOaPYQ0vfqk+NZND+M11BrNmdTgRa9qn8GLT91Ggdu+xt3eAsPRf3w9bJEPa0MUSoptmZvn1Uy171nzcGtdk43ugmoaFuJXsXpWDJMGMQ+hbvmB2N5iZ0ItCyX+uPjNbHy9817ojRZeD/t38RnF4HHb407rbStGygE4GW/Tw84LGJRpfVvzwP3v5Qob3ZUZkvIQ6WvwcFq0GtfmZHo7kRXUegmcPbD3rOhnzlYG4vo51B5GGzozLX2v8aV0rtPzdGgNO0bKITgWaeHLnhub+NoqIzm2BCA6MBlTu2NYg6DhvGQbUeUKy+2dFobkYnAs1cXr7QaAgM2Qm1+xt3y3xdAzZ+A0kJZkeXfZa9DZ4+0FoP261lP50INMcQkA86fAHPb4AiYcYP44R6cPB317/N9MhyOPInNBtmtMTWtGymE4HmWPJXhCcXQe+FxhnyvN4w41E4v9vsyOwj6Rb8+SbkLQv1njc7Gs1N6USgOaayrWDQemg/Bi4dgO+awS8vQuwFsyOzrY3fwJUT0G4UePmYHY3mpnQi0ByXpxfUfQ4G74CGL8GeefB1Lfjnc9for+jaOVgzBso/osdr0EylE4Hm+HLkgjYj4aUtUKYlrBoJ48Jgz3xISTE7uvu3/D1ISYKHPzY7Es3N6USgOY88pYzBbvovgYC8qvs9SQAADJlJREFURud1U1rBmc1mR5Z1p9bDvgXQaCjkKWl2NJqb04lAcz4lGsFzq+Gxb43ilalt4Kf+zjPwTXISLB0GwUWh8StmR6NpOhFoTsrDA2o8AYO3Q7O34PCfML6OMb5v/DWzo7u37dPg4j6juMvH3+xoNE0nAs3J+QRAi/8YCaFKZ1j3JYyrBdumOeaYB3FR8PdIKNkUKnUyOxpNA3Qi0FxFcBF4fCI8twryloHfX4aJTeD4KrMju9vfH8GtWGinB5zRHIdOBJprKVILnloK3WZAwnWY+Rj82AMij5gdGZzbBdunQ72BRsM5TXMQOhForkfEGA3txS1G3z2nN8C3DYwBX8zq0E4po4I4IB80f8ucGDQtHToRaK7L28+4PXPwDqjVF7Z+b16HdnvmQfhmaDXCGH1M0xyITgSa6wsMMUb8uqtDu/pw6I/s6dAu/hr8NRyK1IbqT9h/e5qWRToRaO7jTod2C8DDC+Y+YenQbo99t7vmM7h+Edp9btz2qmkOxq5HpYjkEpEFInJIRA6KSINU80VEvhaRYyKyR0Rq2TMeTQOgbGvj6qD9GLi4H75rCr/aqUO7yCOw6Vuo2QdCa9t+/ZpmA/Y+PRkL/KmUqgBUBw6mmt8OKGv5GwB8a+d4NM1wu0O7ITuhwYuw29Kh3RobdminlNHFtHcAtBxhm3Vqmh3YLRGISDDQFJgCoJRKUEpFp1qsE/CDMmwCcolIIXvFpGn/kiOX0enbi5uhzENGY69xYbDnpwevPzj0Bxz/22jwFhhim3g1zQ7seUVQEogEponIThGZLCIBqZYpAoRbPY+wTLuLiAwQkW0isi0yMtJ+EWvuK29p6DEL+v9h6dDuWZj8AB3aJd6EZf+BkIpQ51nbxqppNmbPROAF1AK+VUrVBOKA+7qBWik1SSkVppQKCwnRZ1aaHZVobHRo12kCxERYOrR7Kusd2q3/GqLPQPvPwNPbLqFqmq3YMxFEABFKqdunVAswEoO1s0BRq+ehlmmaZh4PD6jZ29Kh3ZtweGnWOrSLPgPrvoBKj/2vvfsPsqou4zj+/oAoKSURDKNBoggalm6xEBYWkhWKkjqSP0ihYUgyaG3E/DEOmjM6GUPSjCmB5sJECv0SKscfg/ijphYXBDG0MCDLQaASNGdggn364/u9cLzu3b132eXcc8/zmtnhnu8599zn4bt7n3PPvfc5oaeQc1WuywqBmb0B/EPSKXHo88DGos1WAFfFTw+NAnab2bauism5ihzVC86+GWY2h28qFxrarWlsu6HdE7cACt1FncuArv7U0ExgiaQXgTrgTknTJU2P6x8FNgOvAguBa7o4Hucqd+wAuHgBTHsK+gyG3zSUbmi3+WnYuBzOug56D3zveueqkOxwfLOyE9XX11tzc3PaYbi8MoONj4RvCu96DYaOC0f+fYfA/v/B/NGwbw9c0xRaXDhXJSStMbP61tYdcbiDcS7TJDjtIhh6LjTNDxefv3cU1E+Fo/vAzlfgsoe8CLhM8ULgXEf06Amjr4W6SbDqjtDQzlrg5HPglHPTjs65inghcO5Q9OoHF8yDkV8Pl6A8c4ZfcMZljhcC5zpD/2Fw3py0o3CuQ7wVonPO5ZwXAuecyzkvBM45l3NeCJxzLue8EDjnXM55IXDOuZzzQuCccznnhcA553Iuc03nJO0EKrxKyAF9gX91Yjhp8lyqU63kUit5gOdScIKZtXplr8wVgkMhqblU972s8VyqU63kUit5gOdSDj815JxzOeeFwDnnci5vhWBB2gF0Is+lOtVKLrWSB3gu7crVewTOOefeK2+vCJxzzhXxQuCcczlXs4VA0k8k7ZD0UmKsj6QnJW2K/34wzRjLVSKX2yS9Lmld/DkvzRjLIWmgpFWSNkr6s6SGOJ65eWkjlyzOS09JqyWtj7l8N46fKKlJ0quSlko6Mu1Y29JGHo2StiTmpC7tWMslqbukFyT9Ni53yZzUbCEAGoFxRWM3AivNbAiwMi5nQSPvzQXgbjOriz+PHuaYOmIfcJ2ZDQNGAd+UNIxszkupXCB787IXGGtmZwB1wDhJo4C7CLmcDLwJTE0xxnKUygPg+sScrEsvxIo1AC8nlrtkTmq2EJjZs8B/ioa/DCyKtxcBFx7WoDqoRC6ZY2bbzGxtvP024Rf8w2RwXtrIJXMs+G9c7BF/DBgL/CKOV/28tJFHJkkaAIwH7o/LoovmpGYLQQn9zWxbvP0G0D/NYDrBDEkvxlNHVX86JUnSIOATQBMZn5eiXCCD8xJPQawDdgBPAn8DdpnZvrjJP8lAoSvOw8wKc3JHnJO7JR2VYoiVmAd8B2iJyx+ii+Ykb4XgAAufm83s0QJwHzCY8BJ4GzA33XDKJ6kX8EvgWjN7K7kua/PSSi6ZnBcz229mdcAAYCRwasohdUhxHpI+BtxEyGcE0Ae4IcUQyyLpfGCHma05HI+Xt0KwXdJxAPHfHSnH02Fmtj3+0rcACwl/vFVPUg/CE+cSM/tVHM7kvLSWS1bnpcDMdgGrgDOB3pKOiKsGAK+nFliFEnmMi6fxzMz2Ag+SjTn5DDBB0lbgYcIpoR/SRXOSt0KwApgcb08GlqcYyyEpPHFGFwEvldq2WsRznA8AL5vZDxKrMjcvpXLJ6Lz0k9Q73n4f8AXCex6rgEviZlU/LyXyeCVxkCHCOfWqnxMzu8nMBpjZIOAy4Ckzm0QXzUnNfrNY0kPAGELb1u3ArcAjwDLgI4RW1l8xs6p/E7ZELmMIpx8M2ApcnTjPXpUkjQaeAzZw8LznzYRz65malzZyuZzszcvphDceuxMODpeZ2e2STiIcjfYBXgC+Go+qq1IbeTwF9AMErAOmJ95UrnqSxgCzzOz8rpqTmi0EzjnnypO3U0POOeeKeCFwzrmc80LgnHM554XAOedyzguBc87lnBcClxpJJmluYnmWpNs6ad+Nki5pf8tDfpyJkl6WtKpofFDMb2Zi7B5JU9rZ33RJV7WzzRRJ95RYl5mPRbrq4YXApWkvcLGkvmkHkpT45mY5pgLTzOzsVtbtABoqaRVsZvPNbHEFj99pKszb1RAvBC5N+wjXYP128YriI/rCka6kMZKekbRc0mZJ35M0Kfah3yBpcGI350hqlvTX2Lul0JRsjqTnYxOyqxP7fU7SCmBjK/FcHvf/kqS74thsYDTwgKQ5reS3k9BWe3LxCkmDJT0maU183FPj+G2SZsXbI2KM62LMyW/EHh/vv0nS94v2fbdCP/6VkvrFsTpJf4r7+3WhGZ6kpyXNk9RMKFoTY47rJT3bSk6uBnkhcGn7ETBJ0rEV3OcMYDrwUeBKYKiZjSS0652Z2G4Qoa/MeGC+pJ6EI/jdZjaC0IRsmqQT4/afBBrMbGjywSQdT+gDP5bwreERki40s9uBZmCSmV1fIta7gFmSuheNLwBmmtlwYBZwbyv3fZDwzeQ6YH/RujrgUuDjwKWSBsbxY4BmMzsNeIbwLXSAxcANZnY64dvQtyb2daSZ1ZvZXGA28KXY039CiZxcjfFC4FIVO3YuBr5Vwd2ej43E9hLaJT8RxzcQnvwLlplZi5ltAjYTOlB+EbhKoVVxE6G175C4/Woz29LK440AnjaznbEF8BLgs2Xmtzk+zhWFMYWOpZ8Gfh7j+DGQ7FFE7JnzfjP7Yxz6WdGuV5rZbjPbQ3gFc0IcbwGWxts/BUbHItvbzJ6J44uK4l+auP0HoFHSNEKrBpcDfk7QVYN5wFrCEXDBPuKBiqRuQPI8e7K3SktiuYV3/04X908xQr+ZmWb2eHJF7OfyTsfCb9edhIuJFJ6IuxH6yh/KJROT/wf7Kf23XE4PmQN5m9l0SZ8ivIpaI2m4mf2742G6LPBXBC51scHcMt592b2twPB4ewLhalOVmiipW3zf4CTgL8DjwDcUWkgjaaikY9rZz2rgc5L6xlM8l3PwSb1dZvYK4aj9grj8FrBF0sQYgySdUXSfXcDb8UkZQgfKcnTjYHfKK4Dfm9lu4E1JZ8XxK0vFL2mwmTWZ2WzCexwDW9vO1RZ/ReCqxVxgRmJ5IbBc0nrgMTp2tP4a4Un8A4SOk3sk3U84fbRWkghPdm1e7s/Mtkm6kdACWMDvzKzS9r93ELpFFkwC7pN0C6HIPQysL7rPVGChpBbCE/fuMh7nHcIFWW4hfGrp0jg+mfA+ydGE02RfK3H/OZKGEPJc2UpMrgZ591HnqpSkXoV2ybEQHWdmDSmH5WqQvyJwrnqNl3QT4e/078CUdMNxtcpfETjnXM75m8XOOZdzXgiccy7nvBA451zOeSFwzrmc80LgnHM5938HVvUp7IoiDQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QhBBXvdhLTIy"
},
"source": [
"Grid search on Random Forest Classifier"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VahsTx3aKIYk",
"outputId": "84ebb23c-837f-4e72-b6ab-457f5023b2f3"
},
"source": [
"param_grid = {'criterion':['gini','entropy'],'max_depth': [6],'random_state':[23]}\n",
"GS_RF=GridSearchCV(RandomForestClassifier(), param_grid,cv=5)\n",
"GS_RF.fit(X_train,Y_train)\n",
"GS_RF.best_params_"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'criterion': 'entropy', 'max_depth': 6, 'random_state': 23}"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wq6jLQrBKKDQ",
"outputId": "8e64a57d-63ee-46a5-b925-b765d75206ba"
},
"source": [
"GS_RF.score(X_test,Y_test)"
],
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8863636363636364"
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "k31Wp1OFKKsg"
},
"source": [
"rf_train_score=[]\n",
"rf_test_score=[]\n",
"for i in np.arange(1, 30):\n",
" param_grid = {'criterion':['gini','entropy'],'max_depth': [i],'random_state':[23]}\n",
" GS_RF=GridSearchCV(RandomForestClassifier(), param_grid,cv=5)\n",
" GS_RF.fit(X_train,Y_train)\n",
" y_train_pred=GS_RF.predict(X_train)\n",
" y_pred=GS_RF.predict(X_test)\n",
" rf_train_score.append(log_loss(Y_train,y_train_pred))\n",
" rf_test_score.append(log_loss(Y_test,y_pred))"
],
"execution_count": 60,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"id": "i58xZVIUKLDn",
"outputId": "2c9e263c-d16f-442e-eab8-0ae129f6a5be"
},
"source": [
"plt.title(\"Random Forest Classifier : Error vs Max Depth\")\n",
"plt.xlabel(\"Max Depth\")\n",
"plt.ylabel(\"Error\")\n",
"plt.plot(np.arange(1,30),rf_train_score,label=\"Training Error\")\n",
"plt.plot(np.arange(1,30),rf_test_score,label=\"Testing Error\")\n",
"plt.legend()\n",
"plt.plot()"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QzfcYHhkV6ch"
},
"source": [
"Training model with different activation functions and finding model with best accuracy"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IXS6sCvkT997",
"outputId": "4fe47824-f405-4563-ab64-c0a821b9cbe2"
},
"source": [
"best=1\r\n",
"acc=-1\r\n",
"for a in [\"identity\", \"logistic\", \"tanh\", \"relu\"]:\r\n",
" model = MLPClassifier(activation=a,max_iter=10000, batch_size=64,alpha=0.1,random_state=1).fit(X_train,Y_train)\r\n",
" y_pred = model.predict(X_test)\r\n",
" print(a)\r\n",
" ass(Y_test,y_pred)\r\n",
" score = model.score(X_test,Y_test)\r\n",
" if score>acc:\r\n",
" acc=score\r\n",
" best = a\r\n",
" #print(a,\" - \",model.score(X_test,Y_test))\r\n",
"print(best,acc)"
],
"execution_count": 73,
"outputs": [
{
"output_type": "stream",
"text": [
"identity\n",
"Accuracy: 84.0909090909091\n",
"Sensitivity: 62.96296296296296\n",
"Specificity: 93.44262295081968\n",
"ROC_AUC_Score: 78.20279295689133\n",
"logistic\n",
"Accuracy: 81.81818181818183\n",
"Sensitivity: 66.66666666666666\n",
"Specificity: 88.52459016393442\n",
"ROC_AUC_Score: 77.59562841530054\n",
"tanh\n",
"Accuracy: 84.0909090909091\n",
"Sensitivity: 62.96296296296296\n",
"Specificity: 93.44262295081968\n",
"ROC_AUC_Score: 78.20279295689133\n",
"relu\n",
"Accuracy: 82.95454545454545\n",
"Sensitivity: 70.37037037037037\n",
"Specificity: 88.52459016393442\n",
"ROC_AUC_Score: 79.44748026715239\n",
"identity 0.8409090909090909\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8yTNetDsWQT5"
},
"source": [
"Performing Grid search on the model we got from the above"
]
},
{
"cell_type": "code",
"metadata": {
"id": "2YPLpq7pUBmL"
},
"source": [
"rf_train_score=[]\r\n",
"rf_test_score=[]\r\n",
"a=[0.001,0.01,0.1]\r\n",
"for i in range(len(a)):\r\n",
" param_grid = {'activation':[best],'max_iter': [10000],'batch_size':[64],'alpha':[0.1],'learning_rate_init':[a[i]],'random_state':[1]}\r\n",
" GS=GridSearchCV(MLPClassifier(), param_grid)\r\n",
" GS.fit(X_train,Y_train)\r\n",
" y_train_pred=GS.predict(X_train)\r\n",
" y_pred=GS.predict(X_test)\r\n",
" rf_train_score.append(log_loss(Y_train,y_train_pred))\r\n",
" rf_test_score.append(log_loss(Y_test,y_pred))"
],
"execution_count": 74,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"id": "AcWaCyicUELw",
"outputId": "5735e5b1-3904-42f5-f605-dcf1d502890d"
},
"source": [
"plt.title(\" MLPClassifier Error vs Learning rate\")\r\n",
"plt.xlabel(\"Learning rate\")\r\n",
"plt.ylabel(\"Error\")\r\n",
"plt.plot([0.001,0.01,0.1],rf_train_score,label=\"Training Error\")\r\n",
"plt.plot([0.001,0.01,0.1],rf_test_score,label=\"Testing Error\")\r\n",
"plt.legend()\r\n",
"plt.plot()"
],
"execution_count": 75,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {
"tags": []
},
"execution_count": 75
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
}
]
}