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| a | b/COVID_ICU_Predictions.ipynb | ||
|---|---|---|---|
| 1 | { |
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| 2 | "nbformat": 4, |
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| 3 | "nbformat_minor": 0, |
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| 4 | "metadata": { |
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| 5 | "colab": { |
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| 6 | "provenance": [], |
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| 7 | "authorship_tag": "ABX9TyOAA0GDx4nXZHuugh/P2Zmz", |
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| 8 | "include_colab_link": true |
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| 9 | }, |
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| 10 | "kernelspec": { |
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| 11 | "name": "python3", |
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| 12 | "display_name": "Python 3" |
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| 13 | }, |
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| 14 | "language_info": { |
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| 15 | "name": "python" |
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| 16 | } |
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| 17 | }, |
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| 18 | "cells": [ |
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| 19 | { |
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| 20 | "cell_type": "markdown", |
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| 21 | "metadata": { |
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| 22 | "id": "view-in-github", |
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| 23 | "colab_type": "text" |
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| 24 | }, |
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| 25 | "source": [ |
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| 26 | "<a href=\"https://colab.research.google.com/github/WarrPath/COVID-ICU-Predictions/blob/main/COVID_ICU_Predictions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
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| 27 | ] |
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| 28 | }, |
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| 29 | { |
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| 30 | "cell_type": "markdown", |
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| 31 | "source": [ |
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| 32 | "**Purpose**\n", |
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| 33 | "\n", |
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| 34 | "The COVID-19 pandemic has impacted the whole world, overwhelming healthcare systems and causing us to rethink its organization after having been unprepared for such intense and lengthy requests for ICU beds, the workload burden on professionals, personal protection equipment supply shortages, and limited healthcare resources. \n", |
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| 35 | "\n", |
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| 36 | "Due to rapid increases in COVID-19 cases, ICU units have been nearing or are at capacity. My objective is to use a ML model to predict if a patient of a confirmed COVID-19 case will require admission to ICU in hopes to reduce the strain on the hospital's ICU bed capacity.\n" |
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| 37 | ], |
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| 38 | "metadata": { |
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| 39 | "id": "ZhvP4-_23EBy" |
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| 40 | } |
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| 41 | }, |
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| 42 | { |
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| 43 | "cell_type": "markdown", |
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| 44 | "source": [ |
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| 45 | "**Task**\n", |
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| 46 | "\n", |
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| 47 | "Prepare a Machine Learning model based on the clinical data of confirmed COVID-19 cases from Hospital Sírio-Libanês in Brazil. The database contains health monitoring data of patients in different windows of events and whether or not they are eventually admitted to the intensive care unit (ICU). This will predict the need of ICU for a patient in advance which will help your hospital plan the flow of operations and to make critical decisions for saving lives." |
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| 48 | ], |
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| 49 | "metadata": { |
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| 50 | "id": "DSkixLIoR_6u" |
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| 51 | } |
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| 52 | }, |
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| 53 | { |
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| 54 | "cell_type": "markdown", |
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| 55 | "source": [ |
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| 56 | "**Import Libraries**" |
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| 57 | ], |
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| 58 | "metadata": { |
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| 59 | "id": "AjXwW2ykIW4m" |
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| 60 | } |
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| 61 | }, |
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| 62 | { |
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| 63 | "cell_type": "code", |
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| 64 | "execution_count": 1, |
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| 65 | "metadata": { |
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| 66 | "id": "6c36b7jKmq1n" |
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| 67 | }, |
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| 68 | "outputs": [], |
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| 69 | "source": [ |
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| 70 | "import numpy as np\n", |
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| 71 | "import pandas as pd\n", |
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| 72 | "import matplotlib.pyplot as plt\n", |
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| 73 | "import tabulate\n", |
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| 74 | "import seaborn as sns\n", |
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| 75 | "from sklearn import metrics\n", |
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| 76 | "from sklearn.preprocessing import LabelEncoder\n", |
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| 77 | "from sklearn.linear_model import LogisticRegression\n", |
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| 78 | "from sklearn.neighbors import KNeighborsClassifier\n", |
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| 79 | "from sklearn import tree\n", |
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| 80 | "from sklearn.tree import DecisionTreeClassifier\n", |
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| 81 | "from sklearn.model_selection import train_test_split\n", |
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| 82 | "from sklearn.metrics import classification_report, accuracy_score, roc_auc_score\n", |
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| 83 | "from sklearn.metrics._plot.confusion_matrix import confusion_matrix" |
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| 84 | ] |
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| 85 | }, |
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| 86 | { |
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| 87 | "cell_type": "markdown", |
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| 88 | "source": [ |
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| 89 | "**Upload Dataset**" |
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| 90 | ], |
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| 91 | "metadata": { |
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| 92 | "id": "0sHXdwiGIkvm" |
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| 93 | } |
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| 94 | }, |
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| 95 | { |
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| 96 | "cell_type": "code", |
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| 97 | "source": [ |
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| 98 | "from google.colab import files\n", |
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| 99 | "uploaded = files.upload()" |
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| 100 | ], |
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| 101 | "metadata": { |
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| 102 | "id": "PKvadXn_nCZK", |
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| 103 | "colab": { |
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| 104 | "base_uri": "https://localhost:8080/", |
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| 105 | "height": 73 |
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| 106 | }, |
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| 107 | "outputId": "d6411369-b84b-4220-c212-6d708707d767" |
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| 108 | }, |
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| 109 | "execution_count": 2, |
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| 110 | "outputs": [ |
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| 111 | { |
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| 112 | "output_type": "display_data", |
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| 113 | "data": { |
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| 114 | "text/plain": [ |
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| 115 | "<IPython.core.display.HTML object>" |
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| 116 | ], |
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| 117 | "text/html": [ |
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| 118 | "\n", |
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| 119 | " <input type=\"file\" id=\"files-d5e96163-e57f-4c49-acc9-6d63e635bff3\" name=\"files[]\" multiple disabled\n", |
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| 120 | " style=\"border:none\" />\n", |
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| 121 | " <output id=\"result-d5e96163-e57f-4c49-acc9-6d63e635bff3\">\n", |
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| 122 | " Upload widget is only available when the cell has been executed in the\n", |
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| 123 | " current browser session. Please rerun this cell to enable.\n", |
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| 124 | " </output>\n", |
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| 125 | " <script>// Copyright 2017 Google LLC\n", |
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| 126 | "//\n", |
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| 127 | "// Licensed under the Apache License, Version 2.0 (the \"License\");\n", |
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| 128 | "// you may not use this file except in compliance with the License.\n", |
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| 129 | "// You may obtain a copy of the License at\n", |
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| 130 | "//\n", |
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| 131 | "// http://www.apache.org/licenses/LICENSE-2.0\n", |
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| 132 | "//\n", |
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| 133 | "// Unless required by applicable law or agreed to in writing, software\n", |
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| 134 | "// distributed under the License is distributed on an \"AS IS\" BASIS,\n", |
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| 135 | "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", |
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| 136 | "// See the License for the specific language governing permissions and\n", |
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| 137 | "// limitations under the License.\n", |
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| 138 | "\n", |
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| 139 | "/**\n", |
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| 140 | " * @fileoverview Helpers for google.colab Python module.\n", |
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| 141 | " */\n", |
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| 142 | "(function(scope) {\n", |
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| 143 | "function span(text, styleAttributes = {}) {\n", |
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| 144 | " const element = document.createElement('span');\n", |
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| 145 | " element.textContent = text;\n", |
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| 146 | " for (const key of Object.keys(styleAttributes)) {\n", |
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| 147 | " element.style[key] = styleAttributes[key];\n", |
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| 148 | " }\n", |
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| 149 | " return element;\n", |
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| 150 | "}\n", |
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| 151 | "\n", |
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| 152 | "// Max number of bytes which will be uploaded at a time.\n", |
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| 153 | "const MAX_PAYLOAD_SIZE = 100 * 1024;\n", |
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| 154 | "\n", |
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| 155 | "function _uploadFiles(inputId, outputId) {\n", |
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| 156 | " const steps = uploadFilesStep(inputId, outputId);\n", |
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| 157 | " const outputElement = document.getElementById(outputId);\n", |
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| 158 | " // Cache steps on the outputElement to make it available for the next call\n", |
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| 159 | " // to uploadFilesContinue from Python.\n", |
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| 160 | " outputElement.steps = steps;\n", |
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| 161 | "\n", |
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| 162 | " return _uploadFilesContinue(outputId);\n", |
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| 163 | "}\n", |
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| 164 | "\n", |
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| 165 | "// This is roughly an async generator (not supported in the browser yet),\n", |
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| 166 | "// where there are multiple asynchronous steps and the Python side is going\n", |
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| 167 | "// to poll for completion of each step.\n", |
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| 168 | "// This uses a Promise to block the python side on completion of each step,\n", |
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| 169 | "// then passes the result of the previous step as the input to the next step.\n", |
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| 170 | "function _uploadFilesContinue(outputId) {\n", |
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| 171 | " const outputElement = document.getElementById(outputId);\n", |
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| 172 | " const steps = outputElement.steps;\n", |
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| 173 | "\n", |
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| 174 | " const next = steps.next(outputElement.lastPromiseValue);\n", |
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| 175 | " return Promise.resolve(next.value.promise).then((value) => {\n", |
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| 176 | " // Cache the last promise value to make it available to the next\n", |
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| 177 | " // step of the generator.\n", |
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| 178 | " outputElement.lastPromiseValue = value;\n", |
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| 179 | " return next.value.response;\n", |
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| 180 | " });\n", |
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| 181 | "}\n", |
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| 182 | "\n", |
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| 183 | "/**\n", |
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| 184 | " * Generator function which is called between each async step of the upload\n", |
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| 185 | " * process.\n", |
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| 186 | " * @param {string} inputId Element ID of the input file picker element.\n", |
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| 187 | " * @param {string} outputId Element ID of the output display.\n", |
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| 188 | " * @return {!Iterable<!Object>} Iterable of next steps.\n", |
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| 189 | " */\n", |
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| 190 | "function* uploadFilesStep(inputId, outputId) {\n", |
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| 191 | " const inputElement = document.getElementById(inputId);\n", |
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| 192 | " inputElement.disabled = false;\n", |
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| 193 | "\n", |
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| 194 | " const outputElement = document.getElementById(outputId);\n", |
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| 195 | " outputElement.innerHTML = '';\n", |
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| 196 | "\n", |
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| 197 | " const pickedPromise = new Promise((resolve) => {\n", |
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| 198 | " inputElement.addEventListener('change', (e) => {\n", |
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| 199 | " resolve(e.target.files);\n", |
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| 200 | " });\n", |
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| 201 | " });\n", |
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| 202 | "\n", |
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| 203 | " const cancel = document.createElement('button');\n", |
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| 204 | " inputElement.parentElement.appendChild(cancel);\n", |
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| 205 | " cancel.textContent = 'Cancel upload';\n", |
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| 206 | " const cancelPromise = new Promise((resolve) => {\n", |
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| 207 | " cancel.onclick = () => {\n", |
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| 208 | " resolve(null);\n", |
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| 209 | " };\n", |
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| 210 | " });\n", |
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| 211 | "\n", |
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| 212 | " // Wait for the user to pick the files.\n", |
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| 213 | " const files = yield {\n", |
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| 214 | " promise: Promise.race([pickedPromise, cancelPromise]),\n", |
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| 215 | " response: {\n", |
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| 216 | " action: 'starting',\n", |
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| 217 | " }\n", |
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| 218 | " };\n", |
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| 219 | "\n", |
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| 220 | " cancel.remove();\n", |
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| 221 | "\n", |
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| 222 | " // Disable the input element since further picks are not allowed.\n", |
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| 223 | " inputElement.disabled = true;\n", |
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| 224 | "\n", |
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| 225 | " if (!files) {\n", |
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| 226 | " return {\n", |
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| 227 | " response: {\n", |
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| 228 | " action: 'complete',\n", |
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| 229 | " }\n", |
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| 230 | " };\n", |
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| 231 | " }\n", |
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| 232 | "\n", |
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| 233 | " for (const file of files) {\n", |
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| 234 | " const li = document.createElement('li');\n", |
||
| 235 | " li.append(span(file.name, {fontWeight: 'bold'}));\n", |
||
| 236 | " li.append(span(\n", |
||
| 237 | " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n", |
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| 238 | " `last modified: ${\n", |
||
| 239 | " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n", |
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| 240 | " 'n/a'} - `));\n", |
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| 241 | " const percent = span('0% done');\n", |
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| 242 | " li.appendChild(percent);\n", |
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| 243 | "\n", |
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| 244 | " outputElement.appendChild(li);\n", |
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| 245 | "\n", |
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| 246 | " const fileDataPromise = new Promise((resolve) => {\n", |
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| 247 | " const reader = new FileReader();\n", |
||
| 248 | " reader.onload = (e) => {\n", |
||
| 249 | " resolve(e.target.result);\n", |
||
| 250 | " };\n", |
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| 251 | " reader.readAsArrayBuffer(file);\n", |
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| 252 | " });\n", |
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| 253 | " // Wait for the data to be ready.\n", |
||
| 254 | " let fileData = yield {\n", |
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| 255 | " promise: fileDataPromise,\n", |
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| 256 | " response: {\n", |
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| 257 | " action: 'continue',\n", |
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| 258 | " }\n", |
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| 259 | " };\n", |
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| 260 | "\n", |
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| 261 | " // Use a chunked sending to avoid message size limits. See b/62115660.\n", |
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| 262 | " let position = 0;\n", |
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| 263 | " do {\n", |
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| 264 | " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n", |
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| 265 | " const chunk = new Uint8Array(fileData, position, length);\n", |
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| 266 | " position += length;\n", |
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| 267 | "\n", |
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| 268 | " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n", |
||
| 269 | " yield {\n", |
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| 270 | " response: {\n", |
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| 271 | " action: 'append',\n", |
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| 272 | " file: file.name,\n", |
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| 273 | " data: base64,\n", |
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| 274 | " },\n", |
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| 275 | " };\n", |
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| 276 | "\n", |
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| 277 | " let percentDone = fileData.byteLength === 0 ?\n", |
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| 278 | " 100 :\n", |
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| 279 | " Math.round((position / fileData.byteLength) * 100);\n", |
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| 280 | " percent.textContent = `${percentDone}% done`;\n", |
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| 281 | "\n", |
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| 282 | " } while (position < fileData.byteLength);\n", |
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| 283 | " }\n", |
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| 284 | "\n", |
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| 285 | " // All done.\n", |
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| 286 | " yield {\n", |
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| 287 | " response: {\n", |
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| 288 | " action: 'complete',\n", |
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| 289 | " }\n", |
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| 290 | " };\n", |
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| 291 | "}\n", |
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| 292 | "\n", |
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| 293 | "scope.google = scope.google || {};\n", |
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| 294 | "scope.google.colab = scope.google.colab || {};\n", |
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| 295 | "scope.google.colab._files = {\n", |
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| 296 | " _uploadFiles,\n", |
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| 297 | " _uploadFilesContinue,\n", |
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| 298 | "};\n", |
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| 299 | "})(self);\n", |
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| 300 | "</script> " |
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| 301 | ] |
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| 302 | }, |
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| 303 | "metadata": {} |
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| 304 | }, |
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| 305 | { |
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| 306 | "output_type": "stream", |
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| 307 | "name": "stdout", |
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| 308 | "text": [ |
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| 309 | "Saving Kaggle_Sirio_Libanes_ICU_Prediction.xlsx to Kaggle_Sirio_Libanes_ICU_Prediction.xlsx\n" |
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| 310 | ] |
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| 311 | } |
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| 312 | ] |
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| 313 | }, |
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| 314 | { |
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| 315 | "cell_type": "code", |
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| 316 | "source": [ |
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| 317 | "df = pd.read_excel('Kaggle_Sirio_Libanes_ICU_Prediction.xlsx')" |
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| 318 | ], |
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| 319 | "metadata": { |
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| 320 | "id": "RBWSdkb4nGLk" |
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| 321 | }, |
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| 322 | "execution_count": 3, |
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| 323 | "outputs": [] |
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| 324 | }, |
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| 325 | { |
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| 326 | "cell_type": "markdown", |
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| 327 | "source": [ |
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| 328 | "**Exploratory Data Analysis**\n", |
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| 329 | "\n", |
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| 330 | "Make observations to understand the dataset." |
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| 331 | ], |
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| 332 | "metadata": { |
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| 333 | "id": "AdCMZxRqJD93" |
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| 334 | } |
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| 335 | }, |
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| 336 | { |
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| 337 | "cell_type": "markdown", |
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| 338 | "source": [ |
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| 339 | "# Exploratory Data Analysis\n", |
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| 340 | "\n", |
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| 341 | "Make observations to understand the dataset." |
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| 342 | ], |
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| 343 | "metadata": { |
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| 344 | "id": "ocTFOo8LwTt1" |
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| 345 | } |
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| 346 | }, |
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| 347 | { |
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| 348 | "cell_type": "code", |
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| 349 | "source": [ |
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| 350 | "# View column headers with first 15 rows of the dataset; a glimpse at the features of the dataset\n", |
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| 351 | "df.head(15)" |
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| 352 | ], |
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| 353 | "metadata": { |
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| 354 | "id": "_SrA4UFGpSFe", |
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| 355 | "colab": { |
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| 356 | "base_uri": "https://localhost:8080/", |
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| 357 | "height": 648 |
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| 358 | }, |
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| 359 | "outputId": "b7b0feb4-28df-4167-c94f-f4062194e1e4" |
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| 360 | }, |
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| 361 | "execution_count": 4, |
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| 362 | "outputs": [ |
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| 363 | { |
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| 364 | "output_type": "execute_result", |
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| 365 | "data": { |
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| 366 | "text/plain": [ |
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| 367 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 368 | "0 0 1 60th 0 \n", |
||
| 369 | "1 0 1 60th 0 \n", |
||
| 370 | "2 0 1 60th 0 \n", |
||
| 371 | "3 0 1 60th 0 \n", |
||
| 372 | "4 0 1 60th 0 \n", |
||
| 373 | "5 1 1 90th 1 \n", |
||
| 374 | "6 1 1 90th 1 \n", |
||
| 375 | "7 1 1 90th 1 \n", |
||
| 376 | "8 1 1 90th 1 \n", |
||
| 377 | "9 1 1 90th 1 \n", |
||
| 378 | "10 2 0 10th 0 \n", |
||
| 379 | "11 2 0 10th 0 \n", |
||
| 380 | "12 2 0 10th 0 \n", |
||
| 381 | "13 2 0 10th 0 \n", |
||
| 382 | "14 2 0 10th 0 \n", |
||
| 383 | "\n", |
||
| 384 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 385 | "0 0.0 0.0 0.0 \n", |
||
| 386 | "1 0.0 0.0 0.0 \n", |
||
| 387 | "2 0.0 0.0 0.0 \n", |
||
| 388 | "3 0.0 0.0 0.0 \n", |
||
| 389 | "4 0.0 0.0 0.0 \n", |
||
| 390 | "5 0.0 0.0 0.0 \n", |
||
| 391 | "6 0.0 0.0 0.0 \n", |
||
| 392 | "7 0.0 0.0 0.0 \n", |
||
| 393 | "8 0.0 0.0 0.0 \n", |
||
| 394 | "9 0.0 0.0 0.0 \n", |
||
| 395 | "10 0.0 0.0 0.0 \n", |
||
| 396 | "11 0.0 0.0 0.0 \n", |
||
| 397 | "12 0.0 0.0 0.0 \n", |
||
| 398 | "13 0.0 0.0 0.0 \n", |
||
| 399 | "14 0.0 0.0 0.0 \n", |
||
| 400 | "\n", |
||
| 401 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 402 | "0 0.0 1.0 1.0 ... \n", |
||
| 403 | "1 0.0 1.0 1.0 ... \n", |
||
| 404 | "2 0.0 1.0 1.0 ... \n", |
||
| 405 | "3 0.0 1.0 1.0 ... \n", |
||
| 406 | "4 0.0 1.0 1.0 ... \n", |
||
| 407 | "5 0.0 0.0 0.0 ... \n", |
||
| 408 | "6 0.0 0.0 0.0 ... \n", |
||
| 409 | "7 0.0 0.0 0.0 ... \n", |
||
| 410 | "8 0.0 0.0 0.0 ... \n", |
||
| 411 | "9 0.0 1.0 0.0 ... \n", |
||
| 412 | "10 0.0 0.0 0.0 ... \n", |
||
| 413 | "11 0.0 0.0 0.0 ... \n", |
||
| 414 | "12 0.0 0.0 0.0 ... \n", |
||
| 415 | "13 0.0 0.0 0.0 ... \n", |
||
| 416 | "14 0.0 0.0 0.0 ... \n", |
||
| 417 | "\n", |
||
| 418 | " TEMPERATURE_DIFF OXYGEN_SATURATION_DIFF \\\n", |
||
| 419 | "0 -1.000000 -1.000000 \n", |
||
| 420 | "1 -1.000000 -1.000000 \n", |
||
| 421 | "2 NaN NaN \n", |
||
| 422 | "3 -1.000000 -1.000000 \n", |
||
| 423 | "4 -0.238095 -0.818182 \n", |
||
| 424 | "5 -1.000000 -1.000000 \n", |
||
| 425 | "6 -1.000000 -1.000000 \n", |
||
| 426 | "7 -1.000000 -1.000000 \n", |
||
| 427 | "8 -0.880952 -1.000000 \n", |
||
| 428 | "9 0.142857 -0.797980 \n", |
||
| 429 | "10 NaN NaN \n", |
||
| 430 | "11 NaN NaN \n", |
||
| 431 | "12 NaN -0.959596 \n", |
||
| 432 | "13 -1.000000 -0.797980 \n", |
||
| 433 | "14 -0.500000 -0.898990 \n", |
||
| 434 | "\n", |
||
| 435 | " BLOODPRESSURE_DIASTOLIC_DIFF_REL BLOODPRESSURE_SISTOLIC_DIFF_REL \\\n", |
||
| 436 | "0 -1.000000 -1.000000 \n", |
||
| 437 | "1 -1.000000 -1.000000 \n", |
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| 438 | "2 NaN NaN \n", |
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| 439 | "3 NaN NaN \n", |
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| 440 | "4 -0.389967 0.407558 \n", |
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| 441 | "5 -1.000000 -1.000000 \n", |
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| 442 | "6 -1.000000 -1.000000 \n", |
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| 443 | "7 -1.000000 -1.000000 \n", |
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| 444 | "8 -0.906832 -0.831132 \n", |
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| 445 | "9 0.315690 0.200359 \n", |
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| 446 | "10 NaN NaN \n", |
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| 447 | "11 NaN NaN \n", |
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| 448 | "12 -0.515528 -0.351328 \n", |
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| 449 | "13 -0.658863 -0.563758 \n", |
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| 450 | "14 -0.612422 -0.343258 \n", |
||
| 451 | "\n", |
||
| 452 | " HEART_RATE_DIFF_REL RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 453 | "0 -1.000000 -1.000000 -1.000000 \n", |
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| 454 | "1 -1.000000 -1.000000 -1.000000 \n", |
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| 455 | "2 NaN NaN NaN \n", |
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| 456 | "3 NaN NaN -1.000000 \n", |
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| 457 | "4 -0.230462 0.096774 -0.242282 \n", |
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| 458 | "5 -1.000000 -1.000000 -1.000000 \n", |
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| 459 | "6 -1.000000 -1.000000 -1.000000 \n", |
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| 460 | "7 -1.000000 -1.000000 -1.000000 \n", |
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| 461 | "8 -0.940967 -0.817204 -0.882574 \n", |
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| 462 | "9 -0.239515 0.645161 0.139709 \n", |
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| 463 | "10 NaN NaN NaN \n", |
||
| 464 | "11 NaN NaN NaN \n", |
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| 465 | "12 -0.747001 -0.756272 NaN \n", |
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| 466 | "13 -0.721834 -0.926882 -1.000000 \n", |
||
| 467 | "14 -0.576744 -0.695341 -0.505464 \n", |
||
| 468 | "\n", |
||
| 469 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU \n", |
||
| 470 | "0 -1.000000 0-2 0 \n", |
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| 471 | "1 -1.000000 2-4 0 \n", |
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| 472 | "2 NaN 4-6 0 \n", |
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| 474 | "4 -0.814433 ABOVE_12 1 \n", |
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| 475 | "5 -1.000000 0-2 1 \n", |
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| 476 | "6 -1.000000 2-4 1 \n", |
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| 477 | "7 -1.000000 4-6 1 \n", |
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| 478 | "8 -1.000000 6-12 1 \n", |
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| 479 | "9 -0.802317 ABOVE_12 1 \n", |
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| 480 | "10 NaN 0-2 0 \n", |
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| 481 | "11 NaN 2-4 0 \n", |
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| 482 | "12 -0.961262 4-6 0 \n", |
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| 483 | "13 -0.801293 6-12 0 \n", |
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| 484 | "14 -0.900129 ABOVE_12 1 \n", |
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| 485 | "\n", |
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| 486 | "[15 rows x 231 columns]" |
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| 491 | " <div class=\"colab-df-container\">\n", |
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| 507 | " <thead>\n", |
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| 508 | " <tr style=\"text-align: right;\">\n", |
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| 509 | " <th></th>\n", |
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| 510 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
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| 511 | " <th>AGE_ABOVE65</th>\n", |
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| 520 | " <th>...</th>\n", |
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| 524 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
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| 525 | " <th>HEART_RATE_DIFF_REL</th>\n", |
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| 526 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
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| 527 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
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| 528 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
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| 821 | " </tr>\n", |
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| 822 | " <tr>\n", |
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| 823 | " <th>12</th>\n", |
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| 824 | " <td>2</td>\n", |
||
| 825 | " <td>0</td>\n", |
||
| 826 | " <td>10th</td>\n", |
||
| 827 | " <td>0</td>\n", |
||
| 828 | " <td>0.0</td>\n", |
||
| 829 | " <td>0.0</td>\n", |
||
| 830 | " <td>0.0</td>\n", |
||
| 831 | " <td>0.0</td>\n", |
||
| 832 | " <td>0.0</td>\n", |
||
| 833 | " <td>0.0</td>\n", |
||
| 834 | " <td>...</td>\n", |
||
| 835 | " <td>NaN</td>\n", |
||
| 836 | " <td>-0.959596</td>\n", |
||
| 837 | " <td>-0.515528</td>\n", |
||
| 838 | " <td>-0.351328</td>\n", |
||
| 839 | " <td>-0.747001</td>\n", |
||
| 840 | " <td>-0.756272</td>\n", |
||
| 841 | " <td>NaN</td>\n", |
||
| 842 | " <td>-0.961262</td>\n", |
||
| 843 | " <td>4-6</td>\n", |
||
| 844 | " <td>0</td>\n", |
||
| 845 | " </tr>\n", |
||
| 846 | " <tr>\n", |
||
| 847 | " <th>13</th>\n", |
||
| 848 | " <td>2</td>\n", |
||
| 849 | " <td>0</td>\n", |
||
| 850 | " <td>10th</td>\n", |
||
| 851 | " <td>0</td>\n", |
||
| 852 | " <td>0.0</td>\n", |
||
| 853 | " <td>0.0</td>\n", |
||
| 854 | " <td>0.0</td>\n", |
||
| 855 | " <td>0.0</td>\n", |
||
| 856 | " <td>0.0</td>\n", |
||
| 857 | " <td>0.0</td>\n", |
||
| 858 | " <td>...</td>\n", |
||
| 859 | " <td>-1.000000</td>\n", |
||
| 860 | " <td>-0.797980</td>\n", |
||
| 861 | " <td>-0.658863</td>\n", |
||
| 862 | " <td>-0.563758</td>\n", |
||
| 863 | " <td>-0.721834</td>\n", |
||
| 864 | " <td>-0.926882</td>\n", |
||
| 865 | " <td>-1.000000</td>\n", |
||
| 866 | " <td>-0.801293</td>\n", |
||
| 867 | " <td>6-12</td>\n", |
||
| 868 | " <td>0</td>\n", |
||
| 869 | " </tr>\n", |
||
| 870 | " <tr>\n", |
||
| 871 | " <th>14</th>\n", |
||
| 872 | " <td>2</td>\n", |
||
| 873 | " <td>0</td>\n", |
||
| 874 | " <td>10th</td>\n", |
||
| 875 | " <td>0</td>\n", |
||
| 876 | " <td>0.0</td>\n", |
||
| 877 | " <td>0.0</td>\n", |
||
| 878 | " <td>0.0</td>\n", |
||
| 879 | " <td>0.0</td>\n", |
||
| 880 | " <td>0.0</td>\n", |
||
| 881 | " <td>0.0</td>\n", |
||
| 882 | " <td>...</td>\n", |
||
| 883 | " <td>-0.500000</td>\n", |
||
| 884 | " <td>-0.898990</td>\n", |
||
| 885 | " <td>-0.612422</td>\n", |
||
| 886 | " <td>-0.343258</td>\n", |
||
| 887 | " <td>-0.576744</td>\n", |
||
| 888 | " <td>-0.695341</td>\n", |
||
| 889 | " <td>-0.505464</td>\n", |
||
| 890 | " <td>-0.900129</td>\n", |
||
| 891 | " <td>ABOVE_12</td>\n", |
||
| 892 | " <td>1</td>\n", |
||
| 893 | " </tr>\n", |
||
| 894 | " </tbody>\n", |
||
| 895 | "</table>\n", |
||
| 896 | "<p>15 rows × 231 columns</p>\n", |
||
| 897 | "</div>\n", |
||
| 898 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-084a4d3a-11a6-4cab-8f61-67a901ded0d2')\"\n", |
||
| 899 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 900 | " style=\"display:none;\">\n", |
||
| 901 | " \n", |
||
| 902 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 903 | " width=\"24px\">\n", |
||
| 904 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 905 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 906 | " </svg>\n", |
||
| 907 | " </button>\n", |
||
| 908 | " \n", |
||
| 909 | " <style>\n", |
||
| 910 | " .colab-df-container {\n", |
||
| 911 | " display:flex;\n", |
||
| 912 | " flex-wrap:wrap;\n", |
||
| 913 | " gap: 12px;\n", |
||
| 914 | " }\n", |
||
| 915 | "\n", |
||
| 916 | " .colab-df-convert {\n", |
||
| 917 | " background-color: #E8F0FE;\n", |
||
| 918 | " border: none;\n", |
||
| 919 | " border-radius: 50%;\n", |
||
| 920 | " cursor: pointer;\n", |
||
| 921 | " display: none;\n", |
||
| 922 | " fill: #1967D2;\n", |
||
| 923 | " height: 32px;\n", |
||
| 924 | " padding: 0 0 0 0;\n", |
||
| 925 | " width: 32px;\n", |
||
| 926 | " }\n", |
||
| 927 | "\n", |
||
| 928 | " .colab-df-convert:hover {\n", |
||
| 929 | " background-color: #E2EBFA;\n", |
||
| 930 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 931 | " fill: #174EA6;\n", |
||
| 932 | " }\n", |
||
| 933 | "\n", |
||
| 934 | " [theme=dark] .colab-df-convert {\n", |
||
| 935 | " background-color: #3B4455;\n", |
||
| 936 | " fill: #D2E3FC;\n", |
||
| 937 | " }\n", |
||
| 938 | "\n", |
||
| 939 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 940 | " background-color: #434B5C;\n", |
||
| 941 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 942 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 943 | " fill: #FFFFFF;\n", |
||
| 944 | " }\n", |
||
| 945 | " </style>\n", |
||
| 946 | "\n", |
||
| 947 | " <script>\n", |
||
| 948 | " const buttonEl =\n", |
||
| 949 | " document.querySelector('#df-084a4d3a-11a6-4cab-8f61-67a901ded0d2 button.colab-df-convert');\n", |
||
| 950 | " buttonEl.style.display =\n", |
||
| 951 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 952 | "\n", |
||
| 953 | " async function convertToInteractive(key) {\n", |
||
| 954 | " const element = document.querySelector('#df-084a4d3a-11a6-4cab-8f61-67a901ded0d2');\n", |
||
| 955 | " const dataTable =\n", |
||
| 956 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 957 | " [key], {});\n", |
||
| 958 | " if (!dataTable) return;\n", |
||
| 959 | "\n", |
||
| 960 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 961 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 962 | " + ' to learn more about interactive tables.';\n", |
||
| 963 | " element.innerHTML = '';\n", |
||
| 964 | " dataTable['output_type'] = 'display_data';\n", |
||
| 965 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 966 | " const docLink = document.createElement('div');\n", |
||
| 967 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 968 | " element.appendChild(docLink);\n", |
||
| 969 | " }\n", |
||
| 970 | " </script>\n", |
||
| 971 | " </div>\n", |
||
| 972 | " </div>\n", |
||
| 973 | " " |
||
| 974 | ] |
||
| 975 | }, |
||
| 976 | "metadata": {}, |
||
| 977 | "execution_count": 4 |
||
| 978 | } |
||
| 979 | ] |
||
| 980 | }, |
||
| 981 | { |
||
| 982 | "cell_type": "markdown", |
||
| 983 | "source": [ |
||
| 984 | "Columns AGE_PERCENTIL and WINDOW have non-numerical data that will be transformed later into numerical data.\n", |
||
| 985 | "\n", |
||
| 986 | "There are also null (NaN) values that will need to be filled in for." |
||
| 987 | ], |
||
| 988 | "metadata": { |
||
| 989 | "id": "-g_Gk34sDNN6" |
||
| 990 | } |
||
| 991 | }, |
||
| 992 | { |
||
| 993 | "cell_type": "code", |
||
| 994 | "source": [ |
||
| 995 | "# Show all column headers with the number of entries per column and the data type for the column\n", |
||
| 996 | "df.info(show_counts=True, max_cols=231)" |
||
| 997 | ], |
||
| 998 | "metadata": { |
||
| 999 | "id": "tJbQtwVtnSVm", |
||
| 1000 | "colab": { |
||
| 1001 | "base_uri": "https://localhost:8080/" |
||
| 1002 | }, |
||
| 1003 | "outputId": "64675333-7b71-45ef-fcfd-a784b12e8784" |
||
| 1004 | }, |
||
| 1005 | "execution_count": 5, |
||
| 1006 | "outputs": [ |
||
| 1007 | { |
||
| 1008 | "output_type": "stream", |
||
| 1009 | "name": "stdout", |
||
| 1010 | "text": [ |
||
| 1011 | "<class 'pandas.core.frame.DataFrame'>\n", |
||
| 1012 | "RangeIndex: 1925 entries, 0 to 1924\n", |
||
| 1013 | "Data columns (total 231 columns):\n", |
||
| 1014 | " # Column Non-Null Count Dtype \n", |
||
| 1015 | "--- ------ -------------- ----- \n", |
||
| 1016 | " 0 PATIENT_VISIT_IDENTIFIER 1925 non-null int64 \n", |
||
| 1017 | " 1 AGE_ABOVE65 1925 non-null int64 \n", |
||
| 1018 | " 2 AGE_PERCENTIL 1925 non-null object \n", |
||
| 1019 | " 3 GENDER 1925 non-null int64 \n", |
||
| 1020 | " 4 DISEASE GROUPING 1 1920 non-null float64\n", |
||
| 1021 | " 5 DISEASE GROUPING 2 1920 non-null float64\n", |
||
| 1022 | " 6 DISEASE GROUPING 3 1920 non-null float64\n", |
||
| 1023 | " 7 DISEASE GROUPING 4 1920 non-null float64\n", |
||
| 1024 | " 8 DISEASE GROUPING 5 1920 non-null float64\n", |
||
| 1025 | " 9 DISEASE GROUPING 6 1920 non-null float64\n", |
||
| 1026 | " 10 HTN 1920 non-null float64\n", |
||
| 1027 | " 11 IMMUNOCOMPROMISED 1920 non-null float64\n", |
||
| 1028 | " 12 OTHER 1920 non-null float64\n", |
||
| 1029 | " 13 ALBUMIN_MEDIAN 821 non-null float64\n", |
||
| 1030 | " 14 ALBUMIN_MEAN 821 non-null float64\n", |
||
| 1031 | " 15 ALBUMIN_MIN 821 non-null float64\n", |
||
| 1032 | " 16 ALBUMIN_MAX 821 non-null float64\n", |
||
| 1033 | " 17 ALBUMIN_DIFF 821 non-null float64\n", |
||
| 1034 | " 18 BE_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1035 | " 19 BE_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1036 | " 20 BE_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1037 | " 21 BE_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1038 | " 22 BE_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1039 | " 23 BE_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1040 | " 24 BE_VENOUS_MEAN 821 non-null float64\n", |
||
| 1041 | " 25 BE_VENOUS_MIN 821 non-null float64\n", |
||
| 1042 | " 26 BE_VENOUS_MAX 821 non-null float64\n", |
||
| 1043 | " 27 BE_VENOUS_DIFF 821 non-null float64\n", |
||
| 1044 | " 28 BIC_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1045 | " 29 BIC_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1046 | " 30 BIC_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1047 | " 31 BIC_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1048 | " 32 BIC_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1049 | " 33 BIC_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1050 | " 34 BIC_VENOUS_MEAN 821 non-null float64\n", |
||
| 1051 | " 35 BIC_VENOUS_MIN 821 non-null float64\n", |
||
| 1052 | " 36 BIC_VENOUS_MAX 821 non-null float64\n", |
||
| 1053 | " 37 BIC_VENOUS_DIFF 821 non-null float64\n", |
||
| 1054 | " 38 BILLIRUBIN_MEDIAN 821 non-null float64\n", |
||
| 1055 | " 39 BILLIRUBIN_MEAN 821 non-null float64\n", |
||
| 1056 | " 40 BILLIRUBIN_MIN 821 non-null float64\n", |
||
| 1057 | " 41 BILLIRUBIN_MAX 821 non-null float64\n", |
||
| 1058 | " 42 BILLIRUBIN_DIFF 821 non-null float64\n", |
||
| 1059 | " 43 BLAST_MEDIAN 821 non-null float64\n", |
||
| 1060 | " 44 BLAST_MEAN 821 non-null float64\n", |
||
| 1061 | " 45 BLAST_MIN 821 non-null float64\n", |
||
| 1062 | " 46 BLAST_MAX 821 non-null float64\n", |
||
| 1063 | " 47 BLAST_DIFF 821 non-null float64\n", |
||
| 1064 | " 48 CALCIUM_MEDIAN 821 non-null float64\n", |
||
| 1065 | " 49 CALCIUM_MEAN 821 non-null float64\n", |
||
| 1066 | " 50 CALCIUM_MIN 821 non-null float64\n", |
||
| 1067 | " 51 CALCIUM_MAX 821 non-null float64\n", |
||
| 1068 | " 52 CALCIUM_DIFF 821 non-null float64\n", |
||
| 1069 | " 53 CREATININ_MEDIAN 821 non-null float64\n", |
||
| 1070 | " 54 CREATININ_MEAN 821 non-null float64\n", |
||
| 1071 | " 55 CREATININ_MIN 821 non-null float64\n", |
||
| 1072 | " 56 CREATININ_MAX 821 non-null float64\n", |
||
| 1073 | " 57 CREATININ_DIFF 821 non-null float64\n", |
||
| 1074 | " 58 FFA_MEDIAN 821 non-null float64\n", |
||
| 1075 | " 59 FFA_MEAN 821 non-null float64\n", |
||
| 1076 | " 60 FFA_MIN 821 non-null float64\n", |
||
| 1077 | " 61 FFA_MAX 821 non-null float64\n", |
||
| 1078 | " 62 FFA_DIFF 821 non-null float64\n", |
||
| 1079 | " 63 GGT_MEDIAN 821 non-null float64\n", |
||
| 1080 | " 64 GGT_MEAN 821 non-null float64\n", |
||
| 1081 | " 65 GGT_MIN 821 non-null float64\n", |
||
| 1082 | " 66 GGT_MAX 821 non-null float64\n", |
||
| 1083 | " 67 GGT_DIFF 821 non-null float64\n", |
||
| 1084 | " 68 GLUCOSE_MEDIAN 821 non-null float64\n", |
||
| 1085 | " 69 GLUCOSE_MEAN 821 non-null float64\n", |
||
| 1086 | " 70 GLUCOSE_MIN 821 non-null float64\n", |
||
| 1087 | " 71 GLUCOSE_MAX 821 non-null float64\n", |
||
| 1088 | " 72 GLUCOSE_DIFF 821 non-null float64\n", |
||
| 1089 | " 73 HEMATOCRITE_MEDIAN 821 non-null float64\n", |
||
| 1090 | " 74 HEMATOCRITE_MEAN 821 non-null float64\n", |
||
| 1091 | " 75 HEMATOCRITE_MIN 821 non-null float64\n", |
||
| 1092 | " 76 HEMATOCRITE_MAX 821 non-null float64\n", |
||
| 1093 | " 77 HEMATOCRITE_DIFF 821 non-null float64\n", |
||
| 1094 | " 78 HEMOGLOBIN_MEDIAN 821 non-null float64\n", |
||
| 1095 | " 79 HEMOGLOBIN_MEAN 821 non-null float64\n", |
||
| 1096 | " 80 HEMOGLOBIN_MIN 821 non-null float64\n", |
||
| 1097 | " 81 HEMOGLOBIN_MAX 821 non-null float64\n", |
||
| 1098 | " 82 HEMOGLOBIN_DIFF 821 non-null float64\n", |
||
| 1099 | " 83 INR_MEDIAN 821 non-null float64\n", |
||
| 1100 | " 84 INR_MEAN 821 non-null float64\n", |
||
| 1101 | " 85 INR_MIN 821 non-null float64\n", |
||
| 1102 | " 86 INR_MAX 821 non-null float64\n", |
||
| 1103 | " 87 INR_DIFF 821 non-null float64\n", |
||
| 1104 | " 88 LACTATE_MEDIAN 821 non-null float64\n", |
||
| 1105 | " 89 LACTATE_MEAN 821 non-null float64\n", |
||
| 1106 | " 90 LACTATE_MIN 821 non-null float64\n", |
||
| 1107 | " 91 LACTATE_MAX 821 non-null float64\n", |
||
| 1108 | " 92 LACTATE_DIFF 821 non-null float64\n", |
||
| 1109 | " 93 LEUKOCYTES_MEDIAN 821 non-null float64\n", |
||
| 1110 | " 94 LEUKOCYTES_MEAN 821 non-null float64\n", |
||
| 1111 | " 95 LEUKOCYTES_MIN 821 non-null float64\n", |
||
| 1112 | " 96 LEUKOCYTES_MAX 821 non-null float64\n", |
||
| 1113 | " 97 LEUKOCYTES_DIFF 821 non-null float64\n", |
||
| 1114 | " 98 LINFOCITOS_MEDIAN 821 non-null float64\n", |
||
| 1115 | " 99 LINFOCITOS_MEAN 821 non-null float64\n", |
||
| 1116 | " 100 LINFOCITOS_MIN 821 non-null float64\n", |
||
| 1117 | " 101 LINFOCITOS_MAX 821 non-null float64\n", |
||
| 1118 | " 102 LINFOCITOS_DIFF 821 non-null float64\n", |
||
| 1119 | " 103 NEUTROPHILES_MEDIAN 821 non-null float64\n", |
||
| 1120 | " 104 NEUTROPHILES_MEAN 821 non-null float64\n", |
||
| 1121 | " 105 NEUTROPHILES_MIN 821 non-null float64\n", |
||
| 1122 | " 106 NEUTROPHILES_MAX 821 non-null float64\n", |
||
| 1123 | " 107 NEUTROPHILES_DIFF 821 non-null float64\n", |
||
| 1124 | " 108 P02_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1125 | " 109 P02_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1126 | " 110 P02_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1127 | " 111 P02_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1128 | " 112 P02_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1129 | " 113 P02_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1130 | " 114 P02_VENOUS_MEAN 821 non-null float64\n", |
||
| 1131 | " 115 P02_VENOUS_MIN 821 non-null float64\n", |
||
| 1132 | " 116 P02_VENOUS_MAX 821 non-null float64\n", |
||
| 1133 | " 117 P02_VENOUS_DIFF 821 non-null float64\n", |
||
| 1134 | " 118 PC02_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1135 | " 119 PC02_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1136 | " 120 PC02_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1137 | " 121 PC02_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1138 | " 122 PC02_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1139 | " 123 PC02_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1140 | " 124 PC02_VENOUS_MEAN 821 non-null float64\n", |
||
| 1141 | " 125 PC02_VENOUS_MIN 821 non-null float64\n", |
||
| 1142 | " 126 PC02_VENOUS_MAX 821 non-null float64\n", |
||
| 1143 | " 127 PC02_VENOUS_DIFF 821 non-null float64\n", |
||
| 1144 | " 128 PCR_MEDIAN 821 non-null float64\n", |
||
| 1145 | " 129 PCR_MEAN 821 non-null float64\n", |
||
| 1146 | " 130 PCR_MIN 821 non-null float64\n", |
||
| 1147 | " 131 PCR_MAX 821 non-null float64\n", |
||
| 1148 | " 132 PCR_DIFF 821 non-null float64\n", |
||
| 1149 | " 133 PH_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1150 | " 134 PH_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1151 | " 135 PH_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1152 | " 136 PH_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1153 | " 137 PH_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1154 | " 138 PH_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1155 | " 139 PH_VENOUS_MEAN 821 non-null float64\n", |
||
| 1156 | " 140 PH_VENOUS_MIN 821 non-null float64\n", |
||
| 1157 | " 141 PH_VENOUS_MAX 821 non-null float64\n", |
||
| 1158 | " 142 PH_VENOUS_DIFF 821 non-null float64\n", |
||
| 1159 | " 143 PLATELETS_MEDIAN 821 non-null float64\n", |
||
| 1160 | " 144 PLATELETS_MEAN 821 non-null float64\n", |
||
| 1161 | " 145 PLATELETS_MIN 821 non-null float64\n", |
||
| 1162 | " 146 PLATELETS_MAX 821 non-null float64\n", |
||
| 1163 | " 147 PLATELETS_DIFF 821 non-null float64\n", |
||
| 1164 | " 148 POTASSIUM_MEDIAN 821 non-null float64\n", |
||
| 1165 | " 149 POTASSIUM_MEAN 821 non-null float64\n", |
||
| 1166 | " 150 POTASSIUM_MIN 821 non-null float64\n", |
||
| 1167 | " 151 POTASSIUM_MAX 821 non-null float64\n", |
||
| 1168 | " 152 POTASSIUM_DIFF 821 non-null float64\n", |
||
| 1169 | " 153 SAT02_ARTERIAL_MEDIAN 821 non-null float64\n", |
||
| 1170 | " 154 SAT02_ARTERIAL_MEAN 821 non-null float64\n", |
||
| 1171 | " 155 SAT02_ARTERIAL_MIN 821 non-null float64\n", |
||
| 1172 | " 156 SAT02_ARTERIAL_MAX 821 non-null float64\n", |
||
| 1173 | " 157 SAT02_ARTERIAL_DIFF 821 non-null float64\n", |
||
| 1174 | " 158 SAT02_VENOUS_MEDIAN 821 non-null float64\n", |
||
| 1175 | " 159 SAT02_VENOUS_MEAN 821 non-null float64\n", |
||
| 1176 | " 160 SAT02_VENOUS_MIN 821 non-null float64\n", |
||
| 1177 | " 161 SAT02_VENOUS_MAX 821 non-null float64\n", |
||
| 1178 | " 162 SAT02_VENOUS_DIFF 821 non-null float64\n", |
||
| 1179 | " 163 SODIUM_MEDIAN 821 non-null float64\n", |
||
| 1180 | " 164 SODIUM_MEAN 821 non-null float64\n", |
||
| 1181 | " 165 SODIUM_MIN 821 non-null float64\n", |
||
| 1182 | " 166 SODIUM_MAX 821 non-null float64\n", |
||
| 1183 | " 167 SODIUM_DIFF 821 non-null float64\n", |
||
| 1184 | " 168 TGO_MEDIAN 821 non-null float64\n", |
||
| 1185 | " 169 TGO_MEAN 821 non-null float64\n", |
||
| 1186 | " 170 TGO_MIN 821 non-null float64\n", |
||
| 1187 | " 171 TGO_MAX 821 non-null float64\n", |
||
| 1188 | " 172 TGO_DIFF 821 non-null float64\n", |
||
| 1189 | " 173 TGP_MEDIAN 821 non-null float64\n", |
||
| 1190 | " 174 TGP_MEAN 821 non-null float64\n", |
||
| 1191 | " 175 TGP_MIN 821 non-null float64\n", |
||
| 1192 | " 176 TGP_MAX 821 non-null float64\n", |
||
| 1193 | " 177 TGP_DIFF 821 non-null float64\n", |
||
| 1194 | " 178 TTPA_MEDIAN 821 non-null float64\n", |
||
| 1195 | " 179 TTPA_MEAN 821 non-null float64\n", |
||
| 1196 | " 180 TTPA_MIN 821 non-null float64\n", |
||
| 1197 | " 181 TTPA_MAX 821 non-null float64\n", |
||
| 1198 | " 182 TTPA_DIFF 821 non-null float64\n", |
||
| 1199 | " 183 UREA_MEDIAN 821 non-null float64\n", |
||
| 1200 | " 184 UREA_MEAN 821 non-null float64\n", |
||
| 1201 | " 185 UREA_MIN 821 non-null float64\n", |
||
| 1202 | " 186 UREA_MAX 821 non-null float64\n", |
||
| 1203 | " 187 UREA_DIFF 821 non-null float64\n", |
||
| 1204 | " 188 DIMER_MEDIAN 821 non-null float64\n", |
||
| 1205 | " 189 DIMER_MEAN 821 non-null float64\n", |
||
| 1206 | " 190 DIMER_MIN 821 non-null float64\n", |
||
| 1207 | " 191 DIMER_MAX 821 non-null float64\n", |
||
| 1208 | " 192 DIMER_DIFF 821 non-null float64\n", |
||
| 1209 | " 193 BLOODPRESSURE_DIASTOLIC_MEAN 1240 non-null float64\n", |
||
| 1210 | " 194 BLOODPRESSURE_SISTOLIC_MEAN 1240 non-null float64\n", |
||
| 1211 | " 195 HEART_RATE_MEAN 1240 non-null float64\n", |
||
| 1212 | " 196 RESPIRATORY_RATE_MEAN 1177 non-null float64\n", |
||
| 1213 | " 197 TEMPERATURE_MEAN 1231 non-null float64\n", |
||
| 1214 | " 198 OXYGEN_SATURATION_MEAN 1239 non-null float64\n", |
||
| 1215 | " 199 BLOODPRESSURE_DIASTOLIC_MEDIAN 1240 non-null float64\n", |
||
| 1216 | " 200 BLOODPRESSURE_SISTOLIC_MEDIAN 1240 non-null float64\n", |
||
| 1217 | " 201 HEART_RATE_MEDIAN 1240 non-null float64\n", |
||
| 1218 | " 202 RESPIRATORY_RATE_MEDIAN 1177 non-null float64\n", |
||
| 1219 | " 203 TEMPERATURE_MEDIAN 1231 non-null float64\n", |
||
| 1220 | " 204 OXYGEN_SATURATION_MEDIAN 1239 non-null float64\n", |
||
| 1221 | " 205 BLOODPRESSURE_DIASTOLIC_MIN 1240 non-null float64\n", |
||
| 1222 | " 206 BLOODPRESSURE_SISTOLIC_MIN 1240 non-null float64\n", |
||
| 1223 | " 207 HEART_RATE_MIN 1240 non-null float64\n", |
||
| 1224 | " 208 RESPIRATORY_RATE_MIN 1177 non-null float64\n", |
||
| 1225 | " 209 TEMPERATURE_MIN 1231 non-null float64\n", |
||
| 1226 | " 210 OXYGEN_SATURATION_MIN 1239 non-null float64\n", |
||
| 1227 | " 211 BLOODPRESSURE_DIASTOLIC_MAX 1240 non-null float64\n", |
||
| 1228 | " 212 BLOODPRESSURE_SISTOLIC_MAX 1240 non-null float64\n", |
||
| 1229 | " 213 HEART_RATE_MAX 1240 non-null float64\n", |
||
| 1230 | " 214 RESPIRATORY_RATE_MAX 1177 non-null float64\n", |
||
| 1231 | " 215 TEMPERATURE_MAX 1231 non-null float64\n", |
||
| 1232 | " 216 OXYGEN_SATURATION_MAX 1239 non-null float64\n", |
||
| 1233 | " 217 BLOODPRESSURE_DIASTOLIC_DIFF 1240 non-null float64\n", |
||
| 1234 | " 218 BLOODPRESSURE_SISTOLIC_DIFF 1240 non-null float64\n", |
||
| 1235 | " 219 HEART_RATE_DIFF 1240 non-null float64\n", |
||
| 1236 | " 220 RESPIRATORY_RATE_DIFF 1177 non-null float64\n", |
||
| 1237 | " 221 TEMPERATURE_DIFF 1231 non-null float64\n", |
||
| 1238 | " 222 OXYGEN_SATURATION_DIFF 1239 non-null float64\n", |
||
| 1239 | " 223 BLOODPRESSURE_DIASTOLIC_DIFF_REL 1240 non-null float64\n", |
||
| 1240 | " 224 BLOODPRESSURE_SISTOLIC_DIFF_REL 1240 non-null float64\n", |
||
| 1241 | " 225 HEART_RATE_DIFF_REL 1240 non-null float64\n", |
||
| 1242 | " 226 RESPIRATORY_RATE_DIFF_REL 1177 non-null float64\n", |
||
| 1243 | " 227 TEMPERATURE_DIFF_REL 1231 non-null float64\n", |
||
| 1244 | " 228 OXYGEN_SATURATION_DIFF_REL 1239 non-null float64\n", |
||
| 1245 | " 229 WINDOW 1925 non-null object \n", |
||
| 1246 | " 230 ICU 1925 non-null int64 \n", |
||
| 1247 | "dtypes: float64(225), int64(4), object(2)\n", |
||
| 1248 | "memory usage: 3.4+ MB\n" |
||
| 1249 | ] |
||
| 1250 | } |
||
| 1251 | ] |
||
| 1252 | }, |
||
| 1253 | { |
||
| 1254 | "cell_type": "markdown", |
||
| 1255 | "source": [ |
||
| 1256 | "We can see that there are 231 columns of data:\n", |
||
| 1257 | "\n", |
||
| 1258 | "* 3 are demographics\n", |
||
| 1259 | "* 9 disease groupings (comorbidities)\n", |
||
| 1260 | "* 36 blood test results (each with minimum, maximum, difference, mean, and median)\n", |
||
| 1261 | "* 6 vital signs (each with minimum, maximum, relataive difference, mean, and median)\n", |
||
| 1262 | "\n", |
||
| 1263 | "Again, 2 of the columns are of type oject (non-numerical), and the non-null counts are not all the same which indicates there is null/missing data (counts should all be at 1925).\n", |
||
| 1264 | "\n", |
||
| 1265 | "\n", |
||
| 1266 | "\n" |
||
| 1267 | ], |
||
| 1268 | "metadata": { |
||
| 1269 | "id": "lgLVRkkvDjjv" |
||
| 1270 | } |
||
| 1271 | }, |
||
| 1272 | { |
||
| 1273 | "cell_type": "code", |
||
| 1274 | "source": [ |
||
| 1275 | "# Check for null values\n", |
||
| 1276 | "df.isnull().values.any()" |
||
| 1277 | ], |
||
| 1278 | "metadata": { |
||
| 1279 | "id": "Pdpr-UAnnKHn", |
||
| 1280 | "colab": { |
||
| 1281 | "base_uri": "https://localhost:8080/" |
||
| 1282 | }, |
||
| 1283 | "outputId": "549f3a28-5e26-4ff0-db02-c19dd1ae23f3" |
||
| 1284 | }, |
||
| 1285 | "execution_count": 6, |
||
| 1286 | "outputs": [ |
||
| 1287 | { |
||
| 1288 | "output_type": "execute_result", |
||
| 1289 | "data": { |
||
| 1290 | "text/plain": [ |
||
| 1291 | "True" |
||
| 1292 | ] |
||
| 1293 | }, |
||
| 1294 | "metadata": {}, |
||
| 1295 | "execution_count": 6 |
||
| 1296 | } |
||
| 1297 | ] |
||
| 1298 | }, |
||
| 1299 | { |
||
| 1300 | "cell_type": "markdown", |
||
| 1301 | "source": [ |
||
| 1302 | "Result of \"True\" verifies that there are null values in the dataset.\n", |
||
| 1303 | "\n", |
||
| 1304 | "Null values will be filled when praparing the data for the machine learning model (before implementation)." |
||
| 1305 | ], |
||
| 1306 | "metadata": { |
||
| 1307 | "id": "ZNidopLrh4Hj" |
||
| 1308 | } |
||
| 1309 | }, |
||
| 1310 | { |
||
| 1311 | "cell_type": "code", |
||
| 1312 | "source": [ |
||
| 1313 | "# Check for duplicate values\n", |
||
| 1314 | "df.duplicated().values.any()" |
||
| 1315 | ], |
||
| 1316 | "metadata": { |
||
| 1317 | "id": "XzPqUTcvnTLs", |
||
| 1318 | "colab": { |
||
| 1319 | "base_uri": "https://localhost:8080/" |
||
| 1320 | }, |
||
| 1321 | "outputId": "8de083fd-565f-4725-bf64-999806887e2c" |
||
| 1322 | }, |
||
| 1323 | "execution_count": 7, |
||
| 1324 | "outputs": [ |
||
| 1325 | { |
||
| 1326 | "output_type": "execute_result", |
||
| 1327 | "data": { |
||
| 1328 | "text/plain": [ |
||
| 1329 | "False" |
||
| 1330 | ] |
||
| 1331 | }, |
||
| 1332 | "metadata": {}, |
||
| 1333 | "execution_count": 7 |
||
| 1334 | } |
||
| 1335 | ] |
||
| 1336 | }, |
||
| 1337 | { |
||
| 1338 | "cell_type": "markdown", |
||
| 1339 | "source": [ |
||
| 1340 | "Result of \"False\" indicates that there are no duplicate values." |
||
| 1341 | ], |
||
| 1342 | "metadata": { |
||
| 1343 | "id": "tYV8CS4Uh_Fc" |
||
| 1344 | } |
||
| 1345 | }, |
||
| 1346 | { |
||
| 1347 | "cell_type": "code", |
||
| 1348 | "source": [ |
||
| 1349 | "# Check if patient IDs are repeated\n", |
||
| 1350 | "df['PATIENT_VISIT_IDENTIFIER'].value_counts()" |
||
| 1351 | ], |
||
| 1352 | "metadata": { |
||
| 1353 | "id": "gUmWJvmVQMPd", |
||
| 1354 | "colab": { |
||
| 1355 | "base_uri": "https://localhost:8080/" |
||
| 1356 | }, |
||
| 1357 | "outputId": "c059b901-a3d6-4ec6-fc98-edbd762f4e7b" |
||
| 1358 | }, |
||
| 1359 | "execution_count": 8, |
||
| 1360 | "outputs": [ |
||
| 1361 | { |
||
| 1362 | "output_type": "execute_result", |
||
| 1363 | "data": { |
||
| 1364 | "text/plain": [ |
||
| 1365 | "0 5\n", |
||
| 1366 | "193 5\n", |
||
| 1367 | "263 5\n", |
||
| 1368 | "262 5\n", |
||
| 1369 | "261 5\n", |
||
| 1370 | " ..\n", |
||
| 1371 | "126 5\n", |
||
| 1372 | "125 5\n", |
||
| 1373 | "124 5\n", |
||
| 1374 | "123 5\n", |
||
| 1375 | "384 5\n", |
||
| 1376 | "Name: PATIENT_VISIT_IDENTIFIER, Length: 385, dtype: int64" |
||
| 1377 | ] |
||
| 1378 | }, |
||
| 1379 | "metadata": {}, |
||
| 1380 | "execution_count": 8 |
||
| 1381 | } |
||
| 1382 | ] |
||
| 1383 | }, |
||
| 1384 | { |
||
| 1385 | "cell_type": "markdown", |
||
| 1386 | "source": [ |
||
| 1387 | "Patient IDs are repeated for 5 window time frames with demographic information, blood test and vital sign results, and an indication if the patient was admitted into ICU or not during each window." |
||
| 1388 | ], |
||
| 1389 | "metadata": { |
||
| 1390 | "id": "ECRXPpcHiHxG" |
||
| 1391 | } |
||
| 1392 | }, |
||
| 1393 | { |
||
| 1394 | "cell_type": "code", |
||
| 1395 | "source": [ |
||
| 1396 | "total_patients = len(df.groupby(\"PATIENT_VISIT_IDENTIFIER\").count().index)\n", |
||
| 1397 | "print(f'{total_patients} total patients in the dataset')" |
||
| 1398 | ], |
||
| 1399 | "metadata": { |
||
| 1400 | "id": "zJ5X0CkinafI", |
||
| 1401 | "colab": { |
||
| 1402 | "base_uri": "https://localhost:8080/" |
||
| 1403 | }, |
||
| 1404 | "outputId": "0dd1663f-86c7-48cd-9693-875eb6d0e1e0" |
||
| 1405 | }, |
||
| 1406 | "execution_count": 9, |
||
| 1407 | "outputs": [ |
||
| 1408 | { |
||
| 1409 | "output_type": "stream", |
||
| 1410 | "name": "stdout", |
||
| 1411 | "text": [ |
||
| 1412 | "385 total patients in the dataset\n" |
||
| 1413 | ] |
||
| 1414 | } |
||
| 1415 | ] |
||
| 1416 | }, |
||
| 1417 | { |
||
| 1418 | "cell_type": "code", |
||
| 1419 | "source": [ |
||
| 1420 | "# Percent and number of patients admitted or not admitted into ICU\n", |
||
| 1421 | "ICU_grouped = df[df['WINDOW'] == 'ABOVE_12'].groupby('ICU')['PATIENT_VISIT_IDENTIFIER'].count().reset_index()\n", |
||
| 1422 | "\n", |
||
| 1423 | "labels = [\"not-admitted\", \"admitted\"]\n", |
||
| 1424 | "plt.title('ICU Admissions', fontdict={'fontsize': 16}, pad=45)\n", |
||
| 1425 | "plt.pie(ICU_grouped['PATIENT_VISIT_IDENTIFIER'], textprops={'fontsize': 11}, radius=1.5, labels=labels, startangle=90,\n", |
||
| 1426 | " wedgeprops={'linewidth':1, 'edgecolor':'black'},\n", |
||
| 1427 | " autopct=lambda p: '{:.2f}% \\n({:,.0f}patients)'.format(p,p * sum(ICU_grouped['PATIENT_VISIT_IDENTIFIER'])/100))\n", |
||
| 1428 | "plt.show()" |
||
| 1429 | ], |
||
| 1430 | "metadata": { |
||
| 1431 | "id": "o4w4xZKxmaZ1", |
||
| 1432 | "colab": { |
||
| 1433 | "base_uri": "https://localhost:8080/", |
||
| 1434 | "height": 522 |
||
| 1435 | }, |
||
| 1436 | "outputId": "48f480bd-d571-47b3-b6b2-85f7d9bf5999" |
||
| 1437 | }, |
||
| 1438 | "execution_count": 10, |
||
| 1439 | "outputs": [ |
||
| 1440 | { |
||
| 1441 | "output_type": "display_data", |
||
| 1442 | "data": { |
||
| 1443 | "text/plain": [ |
||
| 1444 | "<Figure size 640x480 with 1 Axes>" |
||
| 1445 | ], |
||
| 1446 | "image/png": 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\n" |
||
| 1447 | }, |
||
| 1448 | "metadata": {} |
||
| 1449 | } |
||
| 1450 | ] |
||
| 1451 | }, |
||
| 1452 | { |
||
| 1453 | "cell_type": "markdown", |
||
| 1454 | "source": [ |
||
| 1455 | "There are about the same number of patients admitted into ICU as those who were not." |
||
| 1456 | ], |
||
| 1457 | "metadata": { |
||
| 1458 | "id": "zo8mC_rGKb3G" |
||
| 1459 | } |
||
| 1460 | }, |
||
| 1461 | { |
||
| 1462 | "cell_type": "code", |
||
| 1463 | "source": [ |
||
| 1464 | "# Indicate which window each patient was admitted into ICU\n", |
||
| 1465 | "ICU_admit = df.groupby('PATIENT_VISIT_IDENTIFIER', as_index = False).agg({'ICU':list, 'WINDOW':list})\n", |
||
| 1466 | "ICU_admit.head(15)" |
||
| 1467 | ], |
||
| 1468 | "metadata": { |
||
| 1469 | "id": "pYM2ihkzoDl7", |
||
| 1470 | "colab": { |
||
| 1471 | "base_uri": "https://localhost:8080/", |
||
| 1472 | "height": 520 |
||
| 1473 | }, |
||
| 1474 | "outputId": "42652055-fa29-4633-b377-67ff7aba2857" |
||
| 1475 | }, |
||
| 1476 | "execution_count": 11, |
||
| 1477 | "outputs": [ |
||
| 1478 | { |
||
| 1479 | "output_type": "execute_result", |
||
| 1480 | "data": { |
||
| 1481 | "text/plain": [ |
||
| 1482 | " PATIENT_VISIT_IDENTIFIER ICU WINDOW\n", |
||
| 1483 | "0 0 [0, 0, 0, 0, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1484 | "1 1 [1, 1, 1, 1, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1485 | "2 2 [0, 0, 0, 0, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1486 | "3 3 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1487 | "4 4 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1488 | "5 5 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1489 | "6 6 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1490 | "7 7 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1491 | "8 8 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1492 | "9 9 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1493 | "10 10 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1494 | "11 11 [0, 0, 0, 1, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1495 | "12 12 [0, 0, 0, 0, 0] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1496 | "13 13 [0, 0, 0, 0, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]\n", |
||
| 1497 | "14 14 [0, 0, 1, 1, 1] [0-2, 2-4, 4-6, 6-12, ABOVE_12]" |
||
| 1498 | ], |
||
| 1499 | "text/html": [ |
||
| 1500 | "\n", |
||
| 1501 | " <div id=\"df-197fc33d-3ea4-489a-86bb-ac2e28cb7b69\">\n", |
||
| 1502 | " <div class=\"colab-df-container\">\n", |
||
| 1503 | " <div>\n", |
||
| 1504 | "<style scoped>\n", |
||
| 1505 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1506 | " vertical-align: middle;\n", |
||
| 1507 | " }\n", |
||
| 1508 | "\n", |
||
| 1509 | " .dataframe tbody tr th {\n", |
||
| 1510 | " vertical-align: top;\n", |
||
| 1511 | " }\n", |
||
| 1512 | "\n", |
||
| 1513 | " .dataframe thead th {\n", |
||
| 1514 | " text-align: right;\n", |
||
| 1515 | " }\n", |
||
| 1516 | "</style>\n", |
||
| 1517 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1518 | " <thead>\n", |
||
| 1519 | " <tr style=\"text-align: right;\">\n", |
||
| 1520 | " <th></th>\n", |
||
| 1521 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 1522 | " <th>ICU</th>\n", |
||
| 1523 | " <th>WINDOW</th>\n", |
||
| 1524 | " </tr>\n", |
||
| 1525 | " </thead>\n", |
||
| 1526 | " <tbody>\n", |
||
| 1527 | " <tr>\n", |
||
| 1528 | " <th>0</th>\n", |
||
| 1529 | " <td>0</td>\n", |
||
| 1530 | " <td>[0, 0, 0, 0, 1]</td>\n", |
||
| 1531 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1532 | " </tr>\n", |
||
| 1533 | " <tr>\n", |
||
| 1534 | " <th>1</th>\n", |
||
| 1535 | " <td>1</td>\n", |
||
| 1536 | " <td>[1, 1, 1, 1, 1]</td>\n", |
||
| 1537 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1538 | " </tr>\n", |
||
| 1539 | " <tr>\n", |
||
| 1540 | " <th>2</th>\n", |
||
| 1541 | " <td>2</td>\n", |
||
| 1542 | " <td>[0, 0, 0, 0, 1]</td>\n", |
||
| 1543 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1544 | " </tr>\n", |
||
| 1545 | " <tr>\n", |
||
| 1546 | " <th>3</th>\n", |
||
| 1547 | " <td>3</td>\n", |
||
| 1548 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1549 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1550 | " </tr>\n", |
||
| 1551 | " <tr>\n", |
||
| 1552 | " <th>4</th>\n", |
||
| 1553 | " <td>4</td>\n", |
||
| 1554 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1555 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1556 | " </tr>\n", |
||
| 1557 | " <tr>\n", |
||
| 1558 | " <th>5</th>\n", |
||
| 1559 | " <td>5</td>\n", |
||
| 1560 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1561 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1562 | " </tr>\n", |
||
| 1563 | " <tr>\n", |
||
| 1564 | " <th>6</th>\n", |
||
| 1565 | " <td>6</td>\n", |
||
| 1566 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1567 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1568 | " </tr>\n", |
||
| 1569 | " <tr>\n", |
||
| 1570 | " <th>7</th>\n", |
||
| 1571 | " <td>7</td>\n", |
||
| 1572 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1573 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1574 | " </tr>\n", |
||
| 1575 | " <tr>\n", |
||
| 1576 | " <th>8</th>\n", |
||
| 1577 | " <td>8</td>\n", |
||
| 1578 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1579 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1580 | " </tr>\n", |
||
| 1581 | " <tr>\n", |
||
| 1582 | " <th>9</th>\n", |
||
| 1583 | " <td>9</td>\n", |
||
| 1584 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1585 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1586 | " </tr>\n", |
||
| 1587 | " <tr>\n", |
||
| 1588 | " <th>10</th>\n", |
||
| 1589 | " <td>10</td>\n", |
||
| 1590 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1591 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1592 | " </tr>\n", |
||
| 1593 | " <tr>\n", |
||
| 1594 | " <th>11</th>\n", |
||
| 1595 | " <td>11</td>\n", |
||
| 1596 | " <td>[0, 0, 0, 1, 1]</td>\n", |
||
| 1597 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1598 | " </tr>\n", |
||
| 1599 | " <tr>\n", |
||
| 1600 | " <th>12</th>\n", |
||
| 1601 | " <td>12</td>\n", |
||
| 1602 | " <td>[0, 0, 0, 0, 0]</td>\n", |
||
| 1603 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1604 | " </tr>\n", |
||
| 1605 | " <tr>\n", |
||
| 1606 | " <th>13</th>\n", |
||
| 1607 | " <td>13</td>\n", |
||
| 1608 | " <td>[0, 0, 0, 0, 1]</td>\n", |
||
| 1609 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1610 | " </tr>\n", |
||
| 1611 | " <tr>\n", |
||
| 1612 | " <th>14</th>\n", |
||
| 1613 | " <td>14</td>\n", |
||
| 1614 | " <td>[0, 0, 1, 1, 1]</td>\n", |
||
| 1615 | " <td>[0-2, 2-4, 4-6, 6-12, ABOVE_12]</td>\n", |
||
| 1616 | " </tr>\n", |
||
| 1617 | " </tbody>\n", |
||
| 1618 | "</table>\n", |
||
| 1619 | "</div>\n", |
||
| 1620 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-197fc33d-3ea4-489a-86bb-ac2e28cb7b69')\"\n", |
||
| 1621 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 1622 | " style=\"display:none;\">\n", |
||
| 1623 | " \n", |
||
| 1624 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 1625 | " width=\"24px\">\n", |
||
| 1626 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 1627 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 1628 | " </svg>\n", |
||
| 1629 | " </button>\n", |
||
| 1630 | " \n", |
||
| 1631 | " <style>\n", |
||
| 1632 | " .colab-df-container {\n", |
||
| 1633 | " display:flex;\n", |
||
| 1634 | " flex-wrap:wrap;\n", |
||
| 1635 | " gap: 12px;\n", |
||
| 1636 | " }\n", |
||
| 1637 | "\n", |
||
| 1638 | " .colab-df-convert {\n", |
||
| 1639 | " background-color: #E8F0FE;\n", |
||
| 1640 | " border: none;\n", |
||
| 1641 | " border-radius: 50%;\n", |
||
| 1642 | " cursor: pointer;\n", |
||
| 1643 | " display: none;\n", |
||
| 1644 | " fill: #1967D2;\n", |
||
| 1645 | " height: 32px;\n", |
||
| 1646 | " padding: 0 0 0 0;\n", |
||
| 1647 | " width: 32px;\n", |
||
| 1648 | " }\n", |
||
| 1649 | "\n", |
||
| 1650 | " .colab-df-convert:hover {\n", |
||
| 1651 | " background-color: #E2EBFA;\n", |
||
| 1652 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 1653 | " fill: #174EA6;\n", |
||
| 1654 | " }\n", |
||
| 1655 | "\n", |
||
| 1656 | " [theme=dark] .colab-df-convert {\n", |
||
| 1657 | " background-color: #3B4455;\n", |
||
| 1658 | " fill: #D2E3FC;\n", |
||
| 1659 | " }\n", |
||
| 1660 | "\n", |
||
| 1661 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 1662 | " background-color: #434B5C;\n", |
||
| 1663 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 1664 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 1665 | " fill: #FFFFFF;\n", |
||
| 1666 | " }\n", |
||
| 1667 | " </style>\n", |
||
| 1668 | "\n", |
||
| 1669 | " <script>\n", |
||
| 1670 | " const buttonEl =\n", |
||
| 1671 | " document.querySelector('#df-197fc33d-3ea4-489a-86bb-ac2e28cb7b69 button.colab-df-convert');\n", |
||
| 1672 | " buttonEl.style.display =\n", |
||
| 1673 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 1674 | "\n", |
||
| 1675 | " async function convertToInteractive(key) {\n", |
||
| 1676 | " const element = document.querySelector('#df-197fc33d-3ea4-489a-86bb-ac2e28cb7b69');\n", |
||
| 1677 | " const dataTable =\n", |
||
| 1678 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 1679 | " [key], {});\n", |
||
| 1680 | " if (!dataTable) return;\n", |
||
| 1681 | "\n", |
||
| 1682 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 1683 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 1684 | " + ' to learn more about interactive tables.';\n", |
||
| 1685 | " element.innerHTML = '';\n", |
||
| 1686 | " dataTable['output_type'] = 'display_data';\n", |
||
| 1687 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 1688 | " const docLink = document.createElement('div');\n", |
||
| 1689 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 1690 | " element.appendChild(docLink);\n", |
||
| 1691 | " }\n", |
||
| 1692 | " </script>\n", |
||
| 1693 | " </div>\n", |
||
| 1694 | " </div>\n", |
||
| 1695 | " " |
||
| 1696 | ] |
||
| 1697 | }, |
||
| 1698 | "metadata": {}, |
||
| 1699 | "execution_count": 11 |
||
| 1700 | } |
||
| 1701 | ] |
||
| 1702 | }, |
||
| 1703 | { |
||
| 1704 | "cell_type": "markdown", |
||
| 1705 | "source": [ |
||
| 1706 | "Patient 0 was admitted into ICU after 12+ hours of being admitted into the hospital.\n", |
||
| 1707 | "\n", |
||
| 1708 | "Patient 1 was admitted into ICU between 0-2 hours after being admitted into the hospital.\n", |
||
| 1709 | "\n", |
||
| 1710 | "Patient 3 was not admitted into ICU.\n", |
||
| 1711 | "\n", |
||
| 1712 | "Patient 11 was admitted into ICU between 6-12 hours after being admitted into the hospital.\n", |
||
| 1713 | "\n", |
||
| 1714 | "Patient 14 was admitted into ICU between 4-6 hours after being admitted into the hospital." |
||
| 1715 | ], |
||
| 1716 | "metadata": { |
||
| 1717 | "id": "8PH8B0__xEPE" |
||
| 1718 | } |
||
| 1719 | }, |
||
| 1720 | { |
||
| 1721 | "cell_type": "code", |
||
| 1722 | "source": [ |
||
| 1723 | "# Number of patients admitted (1) and not admitted (0) into ICU per window time frame\n", |
||
| 1724 | "df.groupby(['WINDOW'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))" |
||
| 1725 | ], |
||
| 1726 | "metadata": { |
||
| 1727 | "id": "r3_UZMe-3AK8", |
||
| 1728 | "colab": { |
||
| 1729 | "base_uri": "https://localhost:8080/", |
||
| 1730 | "height": 238 |
||
| 1731 | }, |
||
| 1732 | "outputId": "811810ec-148a-4fdd-d857-8f6e29db316d" |
||
| 1733 | }, |
||
| 1734 | "execution_count": 12, |
||
| 1735 | "outputs": [ |
||
| 1736 | { |
||
| 1737 | "output_type": "execute_result", |
||
| 1738 | "data": { |
||
| 1739 | "text/plain": [ |
||
| 1740 | "ICU 0 1 Total\n", |
||
| 1741 | "WINDOW \n", |
||
| 1742 | "0-2 353 32 385\n", |
||
| 1743 | "2-4 326 59 385\n", |
||
| 1744 | "4-6 286 99 385\n", |
||
| 1745 | "6-12 255 130 385\n", |
||
| 1746 | "ABOVE_12 190 195 385" |
||
| 1747 | ], |
||
| 1748 | "text/html": [ |
||
| 1749 | "\n", |
||
| 1750 | " <div id=\"df-64508984-fa4b-4426-9571-69760cd3b730\">\n", |
||
| 1751 | " <div class=\"colab-df-container\">\n", |
||
| 1752 | " <div>\n", |
||
| 1753 | "<style scoped>\n", |
||
| 1754 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 1755 | " vertical-align: middle;\n", |
||
| 1756 | " }\n", |
||
| 1757 | "\n", |
||
| 1758 | " .dataframe tbody tr th {\n", |
||
| 1759 | " vertical-align: top;\n", |
||
| 1760 | " }\n", |
||
| 1761 | "\n", |
||
| 1762 | " .dataframe thead th {\n", |
||
| 1763 | " text-align: right;\n", |
||
| 1764 | " }\n", |
||
| 1765 | "</style>\n", |
||
| 1766 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 1767 | " <thead>\n", |
||
| 1768 | " <tr style=\"text-align: right;\">\n", |
||
| 1769 | " <th>ICU</th>\n", |
||
| 1770 | " <th>0</th>\n", |
||
| 1771 | " <th>1</th>\n", |
||
| 1772 | " <th>Total</th>\n", |
||
| 1773 | " </tr>\n", |
||
| 1774 | " <tr>\n", |
||
| 1775 | " <th>WINDOW</th>\n", |
||
| 1776 | " <th></th>\n", |
||
| 1777 | " <th></th>\n", |
||
| 1778 | " <th></th>\n", |
||
| 1779 | " </tr>\n", |
||
| 1780 | " </thead>\n", |
||
| 1781 | " <tbody>\n", |
||
| 1782 | " <tr>\n", |
||
| 1783 | " <th>0-2</th>\n", |
||
| 1784 | " <td>353</td>\n", |
||
| 1785 | " <td>32</td>\n", |
||
| 1786 | " <td>385</td>\n", |
||
| 1787 | " </tr>\n", |
||
| 1788 | " <tr>\n", |
||
| 1789 | " <th>2-4</th>\n", |
||
| 1790 | " <td>326</td>\n", |
||
| 1791 | " <td>59</td>\n", |
||
| 1792 | " <td>385</td>\n", |
||
| 1793 | " </tr>\n", |
||
| 1794 | " <tr>\n", |
||
| 1795 | " <th>4-6</th>\n", |
||
| 1796 | " <td>286</td>\n", |
||
| 1797 | " <td>99</td>\n", |
||
| 1798 | " <td>385</td>\n", |
||
| 1799 | " </tr>\n", |
||
| 1800 | " <tr>\n", |
||
| 1801 | " <th>6-12</th>\n", |
||
| 1802 | " <td>255</td>\n", |
||
| 1803 | " <td>130</td>\n", |
||
| 1804 | " <td>385</td>\n", |
||
| 1805 | " </tr>\n", |
||
| 1806 | " <tr>\n", |
||
| 1807 | " <th>ABOVE_12</th>\n", |
||
| 1808 | " <td>190</td>\n", |
||
| 1809 | " <td>195</td>\n", |
||
| 1810 | " <td>385</td>\n", |
||
| 1811 | " </tr>\n", |
||
| 1812 | " </tbody>\n", |
||
| 1813 | "</table>\n", |
||
| 1814 | "</div>\n", |
||
| 1815 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64508984-fa4b-4426-9571-69760cd3b730')\"\n", |
||
| 1816 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 1817 | " style=\"display:none;\">\n", |
||
| 1818 | " \n", |
||
| 1819 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 1820 | " width=\"24px\">\n", |
||
| 1821 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 1822 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 1823 | " </svg>\n", |
||
| 1824 | " </button>\n", |
||
| 1825 | " \n", |
||
| 1826 | " <style>\n", |
||
| 1827 | " .colab-df-container {\n", |
||
| 1828 | " display:flex;\n", |
||
| 1829 | " flex-wrap:wrap;\n", |
||
| 1830 | " gap: 12px;\n", |
||
| 1831 | " }\n", |
||
| 1832 | "\n", |
||
| 1833 | " .colab-df-convert {\n", |
||
| 1834 | " background-color: #E8F0FE;\n", |
||
| 1835 | " border: none;\n", |
||
| 1836 | " border-radius: 50%;\n", |
||
| 1837 | " cursor: pointer;\n", |
||
| 1838 | " display: none;\n", |
||
| 1839 | " fill: #1967D2;\n", |
||
| 1840 | " height: 32px;\n", |
||
| 1841 | " padding: 0 0 0 0;\n", |
||
| 1842 | " width: 32px;\n", |
||
| 1843 | " }\n", |
||
| 1844 | "\n", |
||
| 1845 | " .colab-df-convert:hover {\n", |
||
| 1846 | " background-color: #E2EBFA;\n", |
||
| 1847 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 1848 | " fill: #174EA6;\n", |
||
| 1849 | " }\n", |
||
| 1850 | "\n", |
||
| 1851 | " [theme=dark] .colab-df-convert {\n", |
||
| 1852 | " background-color: #3B4455;\n", |
||
| 1853 | " fill: #D2E3FC;\n", |
||
| 1854 | " }\n", |
||
| 1855 | "\n", |
||
| 1856 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 1857 | " background-color: #434B5C;\n", |
||
| 1858 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 1859 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 1860 | " fill: #FFFFFF;\n", |
||
| 1861 | " }\n", |
||
| 1862 | " </style>\n", |
||
| 1863 | "\n", |
||
| 1864 | " <script>\n", |
||
| 1865 | " const buttonEl =\n", |
||
| 1866 | " document.querySelector('#df-64508984-fa4b-4426-9571-69760cd3b730 button.colab-df-convert');\n", |
||
| 1867 | " buttonEl.style.display =\n", |
||
| 1868 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 1869 | "\n", |
||
| 1870 | " async function convertToInteractive(key) {\n", |
||
| 1871 | " const element = document.querySelector('#df-64508984-fa4b-4426-9571-69760cd3b730');\n", |
||
| 1872 | " const dataTable =\n", |
||
| 1873 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 1874 | " [key], {});\n", |
||
| 1875 | " if (!dataTable) return;\n", |
||
| 1876 | "\n", |
||
| 1877 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 1878 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 1879 | " + ' to learn more about interactive tables.';\n", |
||
| 1880 | " element.innerHTML = '';\n", |
||
| 1881 | " dataTable['output_type'] = 'display_data';\n", |
||
| 1882 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 1883 | " const docLink = document.createElement('div');\n", |
||
| 1884 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 1885 | " element.appendChild(docLink);\n", |
||
| 1886 | " }\n", |
||
| 1887 | " </script>\n", |
||
| 1888 | " </div>\n", |
||
| 1889 | " </div>\n", |
||
| 1890 | " " |
||
| 1891 | ] |
||
| 1892 | }, |
||
| 1893 | "metadata": {}, |
||
| 1894 | "execution_count": 12 |
||
| 1895 | } |
||
| 1896 | ] |
||
| 1897 | }, |
||
| 1898 | { |
||
| 1899 | "cell_type": "markdown", |
||
| 1900 | "source": [ |
||
| 1901 | "Once a patient is admitted into ICU, they will continue to be in ICU for the subsequent window time frames. In order to obtain the number of *new* patients admitted, subtract the previous window's number of ICU admissions from the desired window's ICU admission count.\n", |
||
| 1902 | "\n", |
||
| 1903 | "\n", |
||
| 1904 | "Number of patients *admitted* into ICU per window time frame:\n", |
||
| 1905 | "\n", |
||
| 1906 | "32 ICU admissions in window 0-2 hours.\n", |
||
| 1907 | "\n", |
||
| 1908 | "27 new ICU admissions in window 2-4 hours.\n", |
||
| 1909 | "\n", |
||
| 1910 | "40 new ICU admissions in window 4-6 hours.\n", |
||
| 1911 | "\n", |
||
| 1912 | "31 new ICU admissions in window 6-12 hours.\n", |
||
| 1913 | "\n", |
||
| 1914 | "65 new ICU admissions in window Above 12 hours.\n", |
||
| 1915 | "\n", |
||
| 1916 | "\n", |
||
| 1917 | "There are nearly twice as many ICU admissions in the Above 12 hour window time frame than any of the other windows." |
||
| 1918 | ], |
||
| 1919 | "metadata": { |
||
| 1920 | "id": "hLPQs9IFLVbG" |
||
| 1921 | } |
||
| 1922 | }, |
||
| 1923 | { |
||
| 1924 | "cell_type": "code", |
||
| 1925 | "source": [ |
||
| 1926 | "# Percent and number of patients per gender\n", |
||
| 1927 | "gender_group = df[df['WINDOW'] == 'ABOVE_12'].groupby('GENDER')['PATIENT_VISIT_IDENTIFIER'].count().reset_index()\n", |
||
| 1928 | "\n", |
||
| 1929 | "labels = [\"Male\", \"Female\"]\n", |
||
| 1930 | "plt.title('Patients Grouped by Gender', fontdict= {'fontsize' : 16}, pad=45)\n", |
||
| 1931 | "plt.pie(gender_group['PATIENT_VISIT_IDENTIFIER'],textprops={'fontsize': 11},radius =1.5, labels = labels, startangle=90,\n", |
||
| 1932 | " wedgeprops={'linewidth':1, 'edgecolor':'black'}, colors=('lightgreen', 'pink'),\n", |
||
| 1933 | " autopct=lambda p : '{:.2f}%\\n({:,.0f}patients)'.format(p,p * sum(gender_group['PATIENT_VISIT_IDENTIFIER'])/100))\n", |
||
| 1934 | "plt.show()" |
||
| 1935 | ], |
||
| 1936 | "metadata": { |
||
| 1937 | "id": "xSnvSt-MY-_l", |
||
| 1938 | "colab": { |
||
| 1939 | "base_uri": "https://localhost:8080/", |
||
| 1940 | "height": 522 |
||
| 1941 | }, |
||
| 1942 | "outputId": "c45a3eb7-e151-4dbc-c3e3-83b45cf0cc57" |
||
| 1943 | }, |
||
| 1944 | "execution_count": 13, |
||
| 1945 | "outputs": [ |
||
| 1946 | { |
||
| 1947 | "output_type": "display_data", |
||
| 1948 | "data": { |
||
| 1949 | "text/plain": [ |
||
| 1950 | "<Figure size 640x480 with 1 Axes>" |
||
| 1951 | ], |
||
| 1952 | "image/png": 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\n" |
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| 1953 | }, |
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| 1954 | "metadata": {} |
||
| 1955 | } |
||
| 1956 | ] |
||
| 1957 | }, |
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| 1958 | { |
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| 1959 | "cell_type": "code", |
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| 1960 | "source": [ |
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| 1961 | "# Percent and number of patients admitted into ICU per gender\n", |
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| 1962 | "ICU_admit = df[df['WINDOW'] == 'ABOVE_12']\n", |
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| 1963 | "\n", |
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| 1964 | "gender_ICU = ICU_admit[ICU_admit['ICU'] == 1]\n", |
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| 1965 | "gender_ICU = gender_ICU.groupby('GENDER')['PATIENT_VISIT_IDENTIFIER'].count().reset_index()\n", |
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| 1966 | "\n", |
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| 1967 | "labels = ['Male', 'Female']\n", |
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| 1968 | "plt.title('ICU Admissions per Gender', fontdict= {'fontsize' : 16}, pad=45)\n", |
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| 1969 | "plt.pie(gender_ICU['PATIENT_VISIT_IDENTIFIER'],textprops={'fontsize': 11},radius =1.5, labels = labels, startangle=90,\n", |
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| 1970 | " wedgeprops={'linewidth':1, 'edgecolor':'black'}, colors=('lightgreen', 'pink'),\n", |
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| 1971 | " autopct=lambda p : '{:.2f}%\\n({:,.0f}patients)'.format(p,p * sum(gender_ICU['PATIENT_VISIT_IDENTIFIER'])/100))\n", |
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| 1972 | "plt.show()" |
||
| 1973 | ], |
||
| 1974 | "metadata": { |
||
| 1975 | "id": "WQe3UJH6NLUs", |
||
| 1976 | "colab": { |
||
| 1977 | "base_uri": "https://localhost:8080/", |
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| 1978 | "height": 522 |
||
| 1979 | }, |
||
| 1980 | "outputId": "27d9dc9f-10b1-46c5-d6dd-ffa6411ed047" |
||
| 1981 | }, |
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| 1982 | "execution_count": 14, |
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| 1983 | "outputs": [ |
||
| 1984 | { |
||
| 1985 | "output_type": "display_data", |
||
| 1986 | "data": { |
||
| 1987 | "text/plain": [ |
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| 1988 | "<Figure size 640x480 with 1 Axes>" |
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| 1989 | ], |
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| 1990 | "image/png": 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\n" |
||
| 1991 | }, |
||
| 1992 | "metadata": {} |
||
| 1993 | } |
||
| 1994 | ] |
||
| 1995 | }, |
||
| 1996 | { |
||
| 1997 | "cell_type": "markdown", |
||
| 1998 | "source": [ |
||
| 1999 | "There are more than twice as many males admitted into ICU than females. However, that is proportional to the number of male and female patients admitted to the hospital." |
||
| 2000 | ], |
||
| 2001 | "metadata": { |
||
| 2002 | "id": "MixH7Cc7Nvo3" |
||
| 2003 | } |
||
| 2004 | }, |
||
| 2005 | { |
||
| 2006 | "cell_type": "code", |
||
| 2007 | "source": [ |
||
| 2008 | "# Percent and number of patients above and below the age of 65\n", |
||
| 2009 | "age_group = df[df['WINDOW'] == 'ABOVE_12'].groupby('AGE_ABOVE65')['PATIENT_VISIT_IDENTIFIER'].count().reset_index()\n", |
||
| 2010 | "\n", |
||
| 2011 | "labels = [\"Below-65\", \"Above-65\"]\n", |
||
| 2012 | "plt.title('Patients Grouped by Age Above/Below 65', fontdict= {'fontsize' : 16}, pad=45)\n", |
||
| 2013 | "plt.pie(age_group['PATIENT_VISIT_IDENTIFIER'],textprops={'fontsize': 11},radius =1.5, labels = labels, startangle=90,\n", |
||
| 2014 | " wedgeprops={'linewidth':1, 'edgecolor':'black'}, colors=('c', 'y'),\n", |
||
| 2015 | " autopct=lambda p : '{:.2f}%\\n({:,.0f}patients)'.format(p,p * sum(age_group['PATIENT_VISIT_IDENTIFIER'])/100))\n", |
||
| 2016 | "plt.show()" |
||
| 2017 | ], |
||
| 2018 | "metadata": { |
||
| 2019 | "id": "n4erUyq2OAEa", |
||
| 2020 | "colab": { |
||
| 2021 | "base_uri": "https://localhost:8080/", |
||
| 2022 | "height": 522 |
||
| 2023 | }, |
||
| 2024 | "outputId": "f0f92a1f-f328-4339-929e-b23da47eabb7" |
||
| 2025 | }, |
||
| 2026 | "execution_count": 15, |
||
| 2027 | "outputs": [ |
||
| 2028 | { |
||
| 2029 | "output_type": "display_data", |
||
| 2030 | "data": { |
||
| 2031 | "text/plain": [ |
||
| 2032 | "<Figure size 640x480 with 1 Axes>" |
||
| 2033 | ], |
||
| 2034 | "image/png": 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\n" |
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| 2035 | }, |
||
| 2036 | "metadata": {} |
||
| 2037 | } |
||
| 2038 | ] |
||
| 2039 | }, |
||
| 2040 | { |
||
| 2041 | "cell_type": "code", |
||
| 2042 | "source": [ |
||
| 2043 | "# Percent and number of patients above and below the age of 65 admitted into ICU\n", |
||
| 2044 | "ICU_admit = df[df['WINDOW'] == 'ABOVE_12']\n", |
||
| 2045 | "\n", |
||
| 2046 | "AGE_65_ICU = ICU_admit[ICU_admit['ICU'] == 1]\n", |
||
| 2047 | "AGE_65_ICU = AGE_65_ICU.groupby('AGE_ABOVE65')['PATIENT_VISIT_IDENTIFIER'].count().reset_index()\n", |
||
| 2048 | "\n", |
||
| 2049 | "labels = [\"Below-65\", \"Above-65\"]\n", |
||
| 2050 | "plt.title('ICU Admissions per Age Above/Below 65', fontdict= {'fontsize' : 16}, pad=45)\n", |
||
| 2051 | "plt.pie(AGE_65_ICU['PATIENT_VISIT_IDENTIFIER'],textprops={'fontsize': 11},radius =1.5, labels = labels, startangle=90,\n", |
||
| 2052 | " wedgeprops={'linewidth':1, 'edgecolor':'black'}, colors=('c', 'y'),\n", |
||
| 2053 | " autopct=lambda p : '{:.2f}%\\n({:,.0f}patients)'.format(p,p * sum(AGE_65_ICU['PATIENT_VISIT_IDENTIFIER'])/100))\n", |
||
| 2054 | "plt.show()" |
||
| 2055 | ], |
||
| 2056 | "metadata": { |
||
| 2057 | "id": "2QE_FBNKONbs", |
||
| 2058 | "colab": { |
||
| 2059 | "base_uri": "https://localhost:8080/", |
||
| 2060 | "height": 522 |
||
| 2061 | }, |
||
| 2062 | "outputId": "9181232c-0391-4ef9-86a8-5b75f80b6589" |
||
| 2063 | }, |
||
| 2064 | "execution_count": 16, |
||
| 2065 | "outputs": [ |
||
| 2066 | { |
||
| 2067 | "output_type": "display_data", |
||
| 2068 | "data": { |
||
| 2069 | "text/plain": [ |
||
| 2070 | "<Figure size 640x480 with 1 Axes>" |
||
| 2071 | ], |
||
| 2072 | "image/png": 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\n" |
||
| 2073 | }, |
||
| 2074 | "metadata": {} |
||
| 2075 | } |
||
| 2076 | ] |
||
| 2077 | }, |
||
| 2078 | { |
||
| 2079 | "cell_type": "markdown", |
||
| 2080 | "source": [ |
||
| 2081 | "Despite there being slightly more patients below the age of 65 admitted to the hospital, there are nearly twice as many patients above the age of 65 admitted into ICU." |
||
| 2082 | ], |
||
| 2083 | "metadata": { |
||
| 2084 | "id": "F5EBItSQOUpQ" |
||
| 2085 | } |
||
| 2086 | }, |
||
| 2087 | { |
||
| 2088 | "cell_type": "code", |
||
| 2089 | "source": [ |
||
| 2090 | "# Total number of patients per age percentile (age bracket)\n", |
||
| 2091 | "age_p = df[df['WINDOW'] == 'ABOVE_12'].groupby('AGE_PERCENTIL')['PATIENT_VISIT_IDENTIFIER'].count()\n", |
||
| 2092 | "print(age_p)" |
||
| 2093 | ], |
||
| 2094 | "metadata": { |
||
| 2095 | "id": "mJLhdFHWO2pi", |
||
| 2096 | "colab": { |
||
| 2097 | "base_uri": "https://localhost:8080/" |
||
| 2098 | }, |
||
| 2099 | "outputId": "909479db-cbf5-4765-db3c-ce0cbabefa51" |
||
| 2100 | }, |
||
| 2101 | "execution_count": 17, |
||
| 2102 | "outputs": [ |
||
| 2103 | { |
||
| 2104 | "output_type": "stream", |
||
| 2105 | "name": "stdout", |
||
| 2106 | "text": [ |
||
| 2107 | "AGE_PERCENTIL\n", |
||
| 2108 | "10th 41\n", |
||
| 2109 | "20th 43\n", |
||
| 2110 | "30th 41\n", |
||
| 2111 | "40th 40\n", |
||
| 2112 | "50th 38\n", |
||
| 2113 | "60th 37\n", |
||
| 2114 | "70th 39\n", |
||
| 2115 | "80th 38\n", |
||
| 2116 | "90th 31\n", |
||
| 2117 | "Above 90th 37\n", |
||
| 2118 | "Name: PATIENT_VISIT_IDENTIFIER, dtype: int64\n" |
||
| 2119 | ] |
||
| 2120 | } |
||
| 2121 | ] |
||
| 2122 | }, |
||
| 2123 | { |
||
| 2124 | "cell_type": "code", |
||
| 2125 | "source": [ |
||
| 2126 | "# Number of patients admitted into ICU per age percentile (age bracket)\n", |
||
| 2127 | "age_p_icu = df[df['WINDOW'] == 'ABOVE_12'].groupby('AGE_PERCENTIL')['ICU'].sum().reset_index()\n", |
||
| 2128 | "print(age_p_icu)" |
||
| 2129 | ], |
||
| 2130 | "metadata": { |
||
| 2131 | "id": "BttiIgX_PL65", |
||
| 2132 | "colab": { |
||
| 2133 | "base_uri": "https://localhost:8080/" |
||
| 2134 | }, |
||
| 2135 | "outputId": "3a1ac78e-436c-46d8-ea42-49bb22990250" |
||
| 2136 | }, |
||
| 2137 | "execution_count": 18, |
||
| 2138 | "outputs": [ |
||
| 2139 | { |
||
| 2140 | "output_type": "stream", |
||
| 2141 | "name": "stdout", |
||
| 2142 | "text": [ |
||
| 2143 | " AGE_PERCENTIL ICU\n", |
||
| 2144 | "0 10th 10\n", |
||
| 2145 | "1 20th 12\n", |
||
| 2146 | "2 30th 18\n", |
||
| 2147 | "3 40th 15\n", |
||
| 2148 | "4 50th 20\n", |
||
| 2149 | "5 60th 20\n", |
||
| 2150 | "6 70th 22\n", |
||
| 2151 | "7 80th 26\n", |
||
| 2152 | "8 90th 23\n", |
||
| 2153 | "9 Above 90th 29\n" |
||
| 2154 | ] |
||
| 2155 | } |
||
| 2156 | ] |
||
| 2157 | }, |
||
| 2158 | { |
||
| 2159 | "cell_type": "markdown", |
||
| 2160 | "source": [ |
||
| 2161 | "Despite the ages of patients admitted to the hospital being evening distributed, there are more ICU admissions as the age percentile (age bracket) increases." |
||
| 2162 | ], |
||
| 2163 | "metadata": { |
||
| 2164 | "id": "ZF_pSfCFPSjv" |
||
| 2165 | } |
||
| 2166 | }, |
||
| 2167 | { |
||
| 2168 | "cell_type": "code", |
||
| 2169 | "source": [ |
||
| 2170 | "# Number of patients admitted and not addmited into ICU for each disease grouping (comorbidity)\n", |
||
| 2171 | "DG1 = df[df['DISEASE GROUPING 1'] == 1].groupby(['DISEASE GROUPING 1'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2172 | "DG2 = df[df['DISEASE GROUPING 2'] == 1].groupby(['DISEASE GROUPING 2'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2173 | "DG3 = df[df['DISEASE GROUPING 3'] == 1].groupby(['DISEASE GROUPING 3'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2174 | "DG4 = df[df['DISEASE GROUPING 4'] == 1].groupby(['DISEASE GROUPING 4'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2175 | "DG5 = df[df['DISEASE GROUPING 5'] == 1].groupby(['DISEASE GROUPING 5'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2176 | "DG6 = df[df['DISEASE GROUPING 6'] == 1].groupby(['DISEASE GROUPING 6'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2177 | "DG_HTN = df[df['HTN'] == 1].groupby(['HTN'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2178 | "DG_IMMUNO = df[df['IMMUNOCOMPROMISED'] == 1].groupby(['IMMUNOCOMPROMISED'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2179 | "DG_OTHER = df[df['OTHER'] == 1].groupby(['OTHER'])['ICU'].value_counts().unstack(fill_value=0).assign(Total = lambda x: x.sum(axis=1))\n", |
||
| 2180 | "\n", |
||
| 2181 | "DG_data = [{'Disease Group 1': DG1, 'Disease Group 2': DG2, 'Disease Group 3': DG3, 'Disease Group 4': DG4, 'Disease Group 5': DG5, 'Disease Group 6': DG6,\n", |
||
| 2182 | " 'HTN Group': DG_HTN, 'Immunocompromised Group': DG_IMMUNO, 'Disease Group Other': DG_OTHER}]\n", |
||
| 2183 | "\n", |
||
| 2184 | "header = DG_data[0].keys()\n", |
||
| 2185 | "rows = [x.values() for x in DG_data]\n", |
||
| 2186 | "print(tabulate.tabulate(rows, header))" |
||
| 2187 | ], |
||
| 2188 | "metadata": { |
||
| 2189 | "id": "D3OvhCniPxvP", |
||
| 2190 | "colab": { |
||
| 2191 | "base_uri": "https://localhost:8080/" |
||
| 2192 | }, |
||
| 2193 | "outputId": "5f66e69a-16e3-443a-fbb2-c147326eed4f" |
||
| 2194 | }, |
||
| 2195 | "execution_count": 19, |
||
| 2196 | "outputs": [ |
||
| 2197 | { |
||
| 2198 | "output_type": "stream", |
||
| 2199 | "name": "stdout", |
||
| 2200 | "text": [ |
||
| 2201 | "Disease Group 1 Disease Group 2 Disease Group 3 Disease Group 4 Disease Group 5 Disease Group 6 HTN Group Immunocompromised Group Disease Group Other\n", |
||
| 2202 | "---------------------------------- --------------------------------- ---------------------------------- --------------------------------- ---------------------------------- --------------------------------- -------------------- --------------------------------- -----------------------\n", |
||
| 2203 | "ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total ICU 0 1 Total\n", |
||
| 2204 | "DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 HTN IMMUNOCOMPROMISED OTHER\n", |
||
| 2205 | "1.0 136 72 208 1.0 28 26 54 1.0 118 70 188 1.0 21 17 38 1.0 155 91 246 1.0 67 23 90 1.0 240 169 409 1.0 218 86 304 1.0 1154 401 1555\n" |
||
| 2206 | ] |
||
| 2207 | } |
||
| 2208 | ] |
||
| 2209 | }, |
||
| 2210 | { |
||
| 2211 | "cell_type": "markdown", |
||
| 2212 | "source": [ |
||
| 2213 | "Patients with hypertension and comorbidities in disease groupings 2 and 4 have the highest percentages of ICU admissions among the different disease groupings (comorbidities)." |
||
| 2214 | ], |
||
| 2215 | "metadata": { |
||
| 2216 | "id": "xBqcFDzD-S1W" |
||
| 2217 | } |
||
| 2218 | }, |
||
| 2219 | { |
||
| 2220 | "cell_type": "markdown", |
||
| 2221 | "source": [ |
||
| 2222 | "While the characteristics of a majority of patients admitted into ICU fall under Gender (male), age (above 65), and particular disease groupings/comorbidities (HTN, Group 2 & 4), there overall is no feature that is ***highly*** correlated with whether or not a patient is going to the intensive care unit. It appears to be a \"synergistic\" combination from multiple variables that results in the need for a patient to be admitted (or not) into ICU. In complex analyses like this, machine learning algorithms can help." |
||
| 2223 | ], |
||
| 2224 | "metadata": { |
||
| 2225 | "id": "YliL7eioLv_n" |
||
| 2226 | } |
||
| 2227 | }, |
||
| 2228 | { |
||
| 2229 | "cell_type": "markdown", |
||
| 2230 | "source": [ |
||
| 2231 | "# Data Preprocessing" |
||
| 2232 | ], |
||
| 2233 | "metadata": { |
||
| 2234 | "id": "9jmtEIs7w0sG" |
||
| 2235 | } |
||
| 2236 | }, |
||
| 2237 | { |
||
| 2238 | "cell_type": "markdown", |
||
| 2239 | "source": [ |
||
| 2240 | "There are values with letters/words (strings) as well as null values (NaN) that need to be processed for machine learning algorithms: \n", |
||
| 2241 | "\n", |
||
| 2242 | "* convert non-numerical data to numeric data by label encoding\n", |
||
| 2243 | "* fill null values with neighboring window values\n", |
||
| 2244 | "\n" |
||
| 2245 | ], |
||
| 2246 | "metadata": { |
||
| 2247 | "id": "_ETi_ToDNEP5" |
||
| 2248 | } |
||
| 2249 | }, |
||
| 2250 | { |
||
| 2251 | "cell_type": "code", |
||
| 2252 | "source": [ |
||
| 2253 | "# Encode/update Age Percentil and Window data to numerical data:\n", |
||
| 2254 | "# e.g. 10th becomes 0, 20th becomes 1, etc.\n", |
||
| 2255 | "# e.g. 0-2 becomes 0, 2-4 becomes 1, etc.\n", |
||
| 2256 | "le = LabelEncoder()\n", |
||
| 2257 | "\n", |
||
| 2258 | "df['AGE_PERCENTIL']=le.fit_transform(df['AGE_PERCENTIL'])\n", |
||
| 2259 | "df['WINDOW']=le.fit_transform(df['WINDOW'])" |
||
| 2260 | ], |
||
| 2261 | "metadata": { |
||
| 2262 | "id": "iI3qWbmYoOkA" |
||
| 2263 | }, |
||
| 2264 | "execution_count": 20, |
||
| 2265 | "outputs": [] |
||
| 2266 | }, |
||
| 2267 | { |
||
| 2268 | "cell_type": "code", |
||
| 2269 | "source": [ |
||
| 2270 | "# Create new column 'ICU_SUM' to indicate if a patient is eventually admitted to ICU\n", |
||
| 2271 | "ICU_sum = (df.groupby('PATIENT_VISIT_IDENTIFIER')['ICU'].sum()>0).reset_index()*1\n", |
||
| 2272 | "ICU_sum.columns = ['PATIENT_VISIT_IDENTIFIER', 'ICU_SUM']\n", |
||
| 2273 | "\n", |
||
| 2274 | "merged_data = pd.merge(df, ICU_sum, on = 'PATIENT_VISIT_IDENTIFIER')" |
||
| 2275 | ], |
||
| 2276 | "metadata": { |
||
| 2277 | "id": "WfUFBE7r91MO" |
||
| 2278 | }, |
||
| 2279 | "execution_count": 21, |
||
| 2280 | "outputs": [] |
||
| 2281 | }, |
||
| 2282 | { |
||
| 2283 | "cell_type": "code", |
||
| 2284 | "source": [ |
||
| 2285 | "# Since the patient's conditions do not vary significantly from one time window to another, we can replicate the data for each individual patient\n", |
||
| 2286 | "# from their neighboring windows.\n", |
||
| 2287 | "filled_data = merged_data.groupby('PATIENT_VISIT_IDENTIFIER', as_index=False)\\\n", |
||
| 2288 | " .fillna(method='ffill')\\\n", |
||
| 2289 | " .fillna(method='bfill')\n", |
||
| 2290 | "\n", |
||
| 2291 | "# Return the PATIENT_VISIT_IDENTIFIER column back to the dataset\n", |
||
| 2292 | "filled_data.insert(0, 'PATIENT_VISIT_IDENTIFIER', df.PATIENT_VISIT_IDENTIFIER)" |
||
| 2293 | ], |
||
| 2294 | "metadata": { |
||
| 2295 | "id": "QTe3quIKyG8c" |
||
| 2296 | }, |
||
| 2297 | "execution_count": 22, |
||
| 2298 | "outputs": [] |
||
| 2299 | }, |
||
| 2300 | { |
||
| 2301 | "cell_type": "code", |
||
| 2302 | "source": [ |
||
| 2303 | "# Show that the dataset now has numerical values for AGE_PERCENTIL and WINDOW columns, a new column ICU_SUM to indicate if a patient is ever admitted\n", |
||
| 2304 | "# into ICU, and null (NaN) values are filled.\n", |
||
| 2305 | "filled_data.head(25)" |
||
| 2306 | ], |
||
| 2307 | "metadata": { |
||
| 2308 | "colab": { |
||
| 2309 | "base_uri": "https://localhost:8080/", |
||
| 2310 | "height": 961 |
||
| 2311 | }, |
||
| 2312 | "id": "yrHDkHhgi7eY", |
||
| 2313 | "outputId": "b08ef40c-b8f8-49a3-8467-378973576eb6" |
||
| 2314 | }, |
||
| 2315 | "execution_count": 23, |
||
| 2316 | "outputs": [ |
||
| 2317 | { |
||
| 2318 | "output_type": "execute_result", |
||
| 2319 | "data": { |
||
| 2320 | "text/plain": [ |
||
| 2321 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 2322 | "0 0 1 5 0 \n", |
||
| 2323 | "1 0 1 5 0 \n", |
||
| 2324 | "2 0 1 5 0 \n", |
||
| 2325 | "3 0 1 5 0 \n", |
||
| 2326 | "4 0 1 5 0 \n", |
||
| 2327 | "5 1 1 8 1 \n", |
||
| 2328 | "6 1 1 8 1 \n", |
||
| 2329 | "7 1 1 8 1 \n", |
||
| 2330 | "8 1 1 8 1 \n", |
||
| 2331 | "9 1 1 8 1 \n", |
||
| 2332 | "10 2 0 0 0 \n", |
||
| 2333 | "11 2 0 0 0 \n", |
||
| 2334 | "12 2 0 0 0 \n", |
||
| 2335 | "13 2 0 0 0 \n", |
||
| 2336 | "14 2 0 0 0 \n", |
||
| 2337 | "15 3 0 3 1 \n", |
||
| 2338 | "16 3 0 3 1 \n", |
||
| 2339 | "17 3 0 3 1 \n", |
||
| 2340 | "18 3 0 3 1 \n", |
||
| 2341 | "19 3 0 3 1 \n", |
||
| 2342 | "20 4 0 0 0 \n", |
||
| 2343 | "21 4 0 0 0 \n", |
||
| 2344 | "22 4 0 0 0 \n", |
||
| 2345 | "23 4 0 0 0 \n", |
||
| 2346 | "24 4 0 0 0 \n", |
||
| 2347 | "\n", |
||
| 2348 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 2349 | "0 0.0 0.0 0.0 \n", |
||
| 2350 | "1 0.0 0.0 0.0 \n", |
||
| 2351 | "2 0.0 0.0 0.0 \n", |
||
| 2352 | "3 0.0 0.0 0.0 \n", |
||
| 2353 | "4 0.0 0.0 0.0 \n", |
||
| 2354 | "5 0.0 0.0 0.0 \n", |
||
| 2355 | "6 0.0 0.0 0.0 \n", |
||
| 2356 | "7 0.0 0.0 0.0 \n", |
||
| 2357 | "8 0.0 0.0 0.0 \n", |
||
| 2358 | "9 0.0 0.0 0.0 \n", |
||
| 2359 | "10 0.0 0.0 0.0 \n", |
||
| 2360 | "11 0.0 0.0 0.0 \n", |
||
| 2361 | "12 0.0 0.0 0.0 \n", |
||
| 2362 | "13 0.0 0.0 0.0 \n", |
||
| 2363 | "14 0.0 0.0 0.0 \n", |
||
| 2364 | "15 0.0 0.0 0.0 \n", |
||
| 2365 | "16 0.0 0.0 0.0 \n", |
||
| 2366 | "17 0.0 0.0 0.0 \n", |
||
| 2367 | "18 0.0 0.0 0.0 \n", |
||
| 2368 | "19 0.0 0.0 0.0 \n", |
||
| 2369 | "20 0.0 0.0 0.0 \n", |
||
| 2370 | "21 0.0 0.0 0.0 \n", |
||
| 2371 | "22 0.0 0.0 0.0 \n", |
||
| 2372 | "23 0.0 0.0 0.0 \n", |
||
| 2373 | "24 0.0 0.0 0.0 \n", |
||
| 2374 | "\n", |
||
| 2375 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 2376 | "0 0.0 1.0 1.0 ... \n", |
||
| 2377 | "1 0.0 1.0 1.0 ... \n", |
||
| 2378 | "2 0.0 1.0 1.0 ... \n", |
||
| 2379 | "3 0.0 1.0 1.0 ... \n", |
||
| 2380 | "4 0.0 1.0 1.0 ... \n", |
||
| 2381 | "5 0.0 0.0 0.0 ... \n", |
||
| 2382 | "6 0.0 0.0 0.0 ... \n", |
||
| 2383 | "7 0.0 0.0 0.0 ... \n", |
||
| 2384 | "8 0.0 0.0 0.0 ... \n", |
||
| 2385 | "9 0.0 1.0 0.0 ... \n", |
||
| 2386 | "10 0.0 0.0 0.0 ... \n", |
||
| 2387 | "11 0.0 0.0 0.0 ... \n", |
||
| 2388 | "12 0.0 0.0 0.0 ... \n", |
||
| 2389 | "13 0.0 0.0 0.0 ... \n", |
||
| 2390 | "14 0.0 0.0 0.0 ... \n", |
||
| 2391 | "15 0.0 0.0 0.0 ... \n", |
||
| 2392 | "16 0.0 0.0 0.0 ... \n", |
||
| 2393 | "17 0.0 0.0 0.0 ... \n", |
||
| 2394 | "18 0.0 0.0 0.0 ... \n", |
||
| 2395 | "19 0.0 0.0 0.0 ... \n", |
||
| 2396 | "20 0.0 0.0 0.0 ... \n", |
||
| 2397 | "21 0.0 0.0 0.0 ... \n", |
||
| 2398 | "22 0.0 0.0 0.0 ... \n", |
||
| 2399 | "23 0.0 0.0 0.0 ... \n", |
||
| 2400 | "24 0.0 0.0 0.0 ... \n", |
||
| 2401 | "\n", |
||
| 2402 | " OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n", |
||
| 2403 | "0 -1.000000 -1.000000 \n", |
||
| 2404 | "1 -1.000000 -1.000000 \n", |
||
| 2405 | "2 -1.000000 -1.000000 \n", |
||
| 2406 | "3 -1.000000 -1.000000 \n", |
||
| 2407 | "4 -0.818182 -0.389967 \n", |
||
| 2408 | "5 -1.000000 -1.000000 \n", |
||
| 2409 | "6 -1.000000 -1.000000 \n", |
||
| 2410 | "7 -1.000000 -1.000000 \n", |
||
| 2411 | "8 -1.000000 -0.906832 \n", |
||
| 2412 | "9 -0.797980 0.315690 \n", |
||
| 2413 | "10 -0.959596 -0.515528 \n", |
||
| 2414 | "11 -0.959596 -0.515528 \n", |
||
| 2415 | "12 -0.959596 -0.515528 \n", |
||
| 2416 | "13 -0.797980 -0.658863 \n", |
||
| 2417 | "14 -0.898990 -0.612422 \n", |
||
| 2418 | "15 -1.000000 -1.000000 \n", |
||
| 2419 | "16 -1.000000 -1.000000 \n", |
||
| 2420 | "17 -1.000000 -1.000000 \n", |
||
| 2421 | "18 -1.000000 -1.000000 \n", |
||
| 2422 | "19 -0.171717 -0.308696 \n", |
||
| 2423 | "20 -0.979798 -1.000000 \n", |
||
| 2424 | "21 -0.979798 -1.000000 \n", |
||
| 2425 | "22 -0.979798 -1.000000 \n", |
||
| 2426 | "23 -0.959596 -1.000000 \n", |
||
| 2427 | "24 -0.939394 -0.652174 \n", |
||
| 2428 | "\n", |
||
| 2429 | " BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n", |
||
| 2430 | "0 -1.000000 -1.000000 \n", |
||
| 2431 | "1 -1.000000 -1.000000 \n", |
||
| 2432 | "2 -1.000000 -1.000000 \n", |
||
| 2433 | "3 -1.000000 -1.000000 \n", |
||
| 2434 | "4 0.407558 -0.230462 \n", |
||
| 2435 | "5 -1.000000 -1.000000 \n", |
||
| 2436 | "6 -1.000000 -1.000000 \n", |
||
| 2437 | "7 -1.000000 -1.000000 \n", |
||
| 2438 | "8 -0.831132 -0.940967 \n", |
||
| 2439 | "9 0.200359 -0.239515 \n", |
||
| 2440 | "10 -0.351328 -0.747001 \n", |
||
| 2441 | "11 -0.351328 -0.747001 \n", |
||
| 2442 | "12 -0.351328 -0.747001 \n", |
||
| 2443 | "13 -0.563758 -0.721834 \n", |
||
| 2444 | "14 -0.343258 -0.576744 \n", |
||
| 2445 | "15 -1.000000 -1.000000 \n", |
||
| 2446 | "16 -1.000000 -1.000000 \n", |
||
| 2447 | "17 -1.000000 -1.000000 \n", |
||
| 2448 | "18 -1.000000 -1.000000 \n", |
||
| 2449 | "19 -0.057718 -0.069094 \n", |
||
| 2450 | "20 -0.883669 -0.956805 \n", |
||
| 2451 | "21 -0.883669 -0.956805 \n", |
||
| 2452 | "22 -0.883669 -0.956805 \n", |
||
| 2453 | "23 -1.000000 -0.926209 \n", |
||
| 2454 | "24 -0.596165 -0.634847 \n", |
||
| 2455 | "\n", |
||
| 2456 | " RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 2457 | "0 -1.000000 -1.000000 \n", |
||
| 2458 | "1 -1.000000 -1.000000 \n", |
||
| 2459 | "2 -1.000000 -1.000000 \n", |
||
| 2460 | "3 -1.000000 -1.000000 \n", |
||
| 2461 | "4 0.096774 -0.242282 \n", |
||
| 2462 | "5 -1.000000 -1.000000 \n", |
||
| 2463 | "6 -1.000000 -1.000000 \n", |
||
| 2464 | "7 -1.000000 -1.000000 \n", |
||
| 2465 | "8 -0.817204 -0.882574 \n", |
||
| 2466 | "9 0.645161 0.139709 \n", |
||
| 2467 | "10 -0.756272 -1.000000 \n", |
||
| 2468 | "11 -0.756272 -1.000000 \n", |
||
| 2469 | "12 -0.756272 -1.000000 \n", |
||
| 2470 | "13 -0.926882 -1.000000 \n", |
||
| 2471 | "14 -0.695341 -0.505464 \n", |
||
| 2472 | "15 -1.000000 -1.000000 \n", |
||
| 2473 | "16 -1.000000 -1.000000 \n", |
||
| 2474 | "17 -1.000000 -1.000000 \n", |
||
| 2475 | "18 -1.000000 -1.000000 \n", |
||
| 2476 | "19 -0.329749 -0.047619 \n", |
||
| 2477 | "20 -0.870968 -0.953536 \n", |
||
| 2478 | "21 -0.870968 -0.953536 \n", |
||
| 2479 | "22 -0.870968 -0.953536 \n", |
||
| 2480 | "23 -1.000000 -0.698797 \n", |
||
| 2481 | "24 -0.817204 -0.645793 \n", |
||
| 2482 | "\n", |
||
| 2483 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU ICU_SUM \n", |
||
| 2484 | "0 -1.000000 0 0 1 \n", |
||
| 2485 | "1 -1.000000 1 0 1 \n", |
||
| 2486 | "2 -1.000000 2 0 1 \n", |
||
| 2487 | "3 -1.000000 3 0 1 \n", |
||
| 2488 | "4 -0.814433 4 1 1 \n", |
||
| 2489 | "5 -1.000000 0 1 1 \n", |
||
| 2490 | "6 -1.000000 1 1 1 \n", |
||
| 2491 | "7 -1.000000 2 1 1 \n", |
||
| 2492 | "8 -1.000000 3 1 1 \n", |
||
| 2493 | "9 -0.802317 4 1 1 \n", |
||
| 2494 | "10 -0.961262 0 0 1 \n", |
||
| 2495 | "11 -0.961262 1 0 1 \n", |
||
| 2496 | "12 -0.961262 2 0 1 \n", |
||
| 2497 | "13 -0.801293 3 0 1 \n", |
||
| 2498 | "14 -0.900129 4 1 1 \n", |
||
| 2499 | "15 -1.000000 0 0 0 \n", |
||
| 2500 | "16 -1.000000 1 0 0 \n", |
||
| 2501 | "17 -1.000000 2 0 0 \n", |
||
| 2502 | "18 -1.000000 3 0 0 \n", |
||
| 2503 | "19 -0.172436 4 0 0 \n", |
||
| 2504 | "20 -0.980333 0 0 0 \n", |
||
| 2505 | "21 -0.980333 1 0 0 \n", |
||
| 2506 | "22 -0.980333 2 0 0 \n", |
||
| 2507 | "23 -0.960463 3 0 0 \n", |
||
| 2508 | "24 -0.940077 4 0 0 \n", |
||
| 2509 | "\n", |
||
| 2510 | "[25 rows x 232 columns]" |
||
| 2511 | ], |
||
| 2512 | "text/html": [ |
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| 2513 | "\n", |
||
| 2514 | " <div id=\"df-62c5ddb9-3675-4f84-b74d-a64dc4b552ae\">\n", |
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| 2515 | " <div class=\"colab-df-container\">\n", |
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| 2516 | " <div>\n", |
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| 2517 | "<style scoped>\n", |
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| 2518 | " .dataframe tbody tr th:only-of-type {\n", |
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| 2519 | " vertical-align: middle;\n", |
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| 2527 | " text-align: right;\n", |
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| 2528 | " }\n", |
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| 2529 | "</style>\n", |
||
| 2530 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 2531 | " <thead>\n", |
||
| 2532 | " <tr style=\"text-align: right;\">\n", |
||
| 2533 | " <th></th>\n", |
||
| 2534 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 2535 | " <th>AGE_ABOVE65</th>\n", |
||
| 2536 | " <th>AGE_PERCENTIL</th>\n", |
||
| 2537 | " <th>GENDER</th>\n", |
||
| 2538 | " <th>DISEASE GROUPING 1</th>\n", |
||
| 2539 | " <th>DISEASE GROUPING 2</th>\n", |
||
| 2540 | " <th>DISEASE GROUPING 3</th>\n", |
||
| 2541 | " <th>DISEASE GROUPING 4</th>\n", |
||
| 2542 | " <th>DISEASE GROUPING 5</th>\n", |
||
| 2543 | " <th>DISEASE GROUPING 6</th>\n", |
||
| 2544 | " <th>...</th>\n", |
||
| 2545 | " <th>OXYGEN_SATURATION_DIFF</th>\n", |
||
| 2546 | " <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n", |
||
| 2547 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
||
| 2548 | " <th>HEART_RATE_DIFF_REL</th>\n", |
||
| 2549 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
||
| 2550 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
||
| 2551 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
||
| 2552 | " <th>WINDOW</th>\n", |
||
| 2553 | " <th>ICU</th>\n", |
||
| 2554 | " <th>ICU_SUM</th>\n", |
||
| 2555 | " </tr>\n", |
||
| 2556 | " </thead>\n", |
||
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||
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| 2574 | " <td>-1.000000</td>\n", |
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| 2575 | " <td>-1.000000</td>\n", |
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| 2576 | " <td>-1.000000</td>\n", |
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| 2578 | " <td>0</td>\n", |
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| 2581 | " </tr>\n", |
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| 2582 | " <tr>\n", |
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||
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| 2586 | " <td>5</td>\n", |
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| 2587 | " <td>0</td>\n", |
||
| 2588 | " <td>0.0</td>\n", |
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| 2591 | " <td>0.0</td>\n", |
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| 2593 | " <td>1.0</td>\n", |
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| 2595 | " <td>-1.000000</td>\n", |
||
| 2596 | " <td>-1.000000</td>\n", |
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| 2597 | " <td>-1.000000</td>\n", |
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| 2598 | " <td>-1.000000</td>\n", |
||
| 2599 | " <td>-1.000000</td>\n", |
||
| 2600 | " <td>-1.000000</td>\n", |
||
| 2601 | " <td>-1.000000</td>\n", |
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| 2603 | " <td>0</td>\n", |
||
| 2604 | " <td>1</td>\n", |
||
| 2605 | " </tr>\n", |
||
| 2606 | " <tr>\n", |
||
| 2607 | " <th>2</th>\n", |
||
| 2608 | " <td>0</td>\n", |
||
| 2609 | " <td>1</td>\n", |
||
| 2610 | " <td>5</td>\n", |
||
| 2611 | " <td>0</td>\n", |
||
| 2612 | " <td>0.0</td>\n", |
||
| 2613 | " <td>0.0</td>\n", |
||
| 2614 | " <td>0.0</td>\n", |
||
| 2615 | " <td>0.0</td>\n", |
||
| 2616 | " <td>1.0</td>\n", |
||
| 2617 | " <td>1.0</td>\n", |
||
| 2618 | " <td>...</td>\n", |
||
| 2619 | " <td>-1.000000</td>\n", |
||
| 2620 | " <td>-1.000000</td>\n", |
||
| 2621 | " <td>-1.000000</td>\n", |
||
| 2622 | " <td>-1.000000</td>\n", |
||
| 2623 | " <td>-1.000000</td>\n", |
||
| 2624 | " <td>-1.000000</td>\n", |
||
| 2625 | " <td>-1.000000</td>\n", |
||
| 2626 | " <td>2</td>\n", |
||
| 2627 | " <td>0</td>\n", |
||
| 2628 | " <td>1</td>\n", |
||
| 2629 | " </tr>\n", |
||
| 2630 | " <tr>\n", |
||
| 2631 | " <th>3</th>\n", |
||
| 2632 | " <td>0</td>\n", |
||
| 2633 | " <td>1</td>\n", |
||
| 2634 | " <td>5</td>\n", |
||
| 2635 | " <td>0</td>\n", |
||
| 2636 | " <td>0.0</td>\n", |
||
| 2637 | " <td>0.0</td>\n", |
||
| 2638 | " <td>0.0</td>\n", |
||
| 2639 | " <td>0.0</td>\n", |
||
| 2640 | " <td>1.0</td>\n", |
||
| 2641 | " <td>1.0</td>\n", |
||
| 2642 | " <td>...</td>\n", |
||
| 2643 | " <td>-1.000000</td>\n", |
||
| 2644 | " <td>-1.000000</td>\n", |
||
| 2645 | " <td>-1.000000</td>\n", |
||
| 2646 | " <td>-1.000000</td>\n", |
||
| 2647 | " <td>-1.000000</td>\n", |
||
| 2648 | " <td>-1.000000</td>\n", |
||
| 2649 | " <td>-1.000000</td>\n", |
||
| 2650 | " <td>3</td>\n", |
||
| 2651 | " <td>0</td>\n", |
||
| 2652 | " <td>1</td>\n", |
||
| 2653 | " </tr>\n", |
||
| 2654 | " <tr>\n", |
||
| 2655 | " <th>4</th>\n", |
||
| 2656 | " <td>0</td>\n", |
||
| 2657 | " <td>1</td>\n", |
||
| 2658 | " <td>5</td>\n", |
||
| 2659 | " <td>0</td>\n", |
||
| 2660 | " <td>0.0</td>\n", |
||
| 2661 | " <td>0.0</td>\n", |
||
| 2662 | " <td>0.0</td>\n", |
||
| 2663 | " <td>0.0</td>\n", |
||
| 2664 | " <td>1.0</td>\n", |
||
| 2665 | " <td>1.0</td>\n", |
||
| 2666 | " <td>...</td>\n", |
||
| 2667 | " <td>-0.818182</td>\n", |
||
| 2668 | " <td>-0.389967</td>\n", |
||
| 2669 | " <td>0.407558</td>\n", |
||
| 2670 | " <td>-0.230462</td>\n", |
||
| 2671 | " <td>0.096774</td>\n", |
||
| 2672 | " <td>-0.242282</td>\n", |
||
| 2673 | " <td>-0.814433</td>\n", |
||
| 2674 | " <td>4</td>\n", |
||
| 2675 | " <td>1</td>\n", |
||
| 2676 | " <td>1</td>\n", |
||
| 2677 | " </tr>\n", |
||
| 2678 | " <tr>\n", |
||
| 2679 | " <th>5</th>\n", |
||
| 2680 | " <td>1</td>\n", |
||
| 2681 | " <td>1</td>\n", |
||
| 2682 | " <td>8</td>\n", |
||
| 2683 | " <td>1</td>\n", |
||
| 2684 | " <td>0.0</td>\n", |
||
| 2685 | " <td>0.0</td>\n", |
||
| 2686 | " <td>0.0</td>\n", |
||
| 2687 | " <td>0.0</td>\n", |
||
| 2688 | " <td>0.0</td>\n", |
||
| 2689 | " <td>0.0</td>\n", |
||
| 2690 | " <td>...</td>\n", |
||
| 2691 | " <td>-1.000000</td>\n", |
||
| 2692 | " <td>-1.000000</td>\n", |
||
| 2693 | " <td>-1.000000</td>\n", |
||
| 2694 | " <td>-1.000000</td>\n", |
||
| 2695 | " <td>-1.000000</td>\n", |
||
| 2696 | " <td>-1.000000</td>\n", |
||
| 2697 | " <td>-1.000000</td>\n", |
||
| 2698 | " <td>0</td>\n", |
||
| 2699 | " <td>1</td>\n", |
||
| 2700 | " <td>1</td>\n", |
||
| 2701 | " </tr>\n", |
||
| 2702 | " <tr>\n", |
||
| 2703 | " <th>6</th>\n", |
||
| 2704 | " <td>1</td>\n", |
||
| 2705 | " <td>1</td>\n", |
||
| 2706 | " <td>8</td>\n", |
||
| 2707 | " <td>1</td>\n", |
||
| 2708 | " <td>0.0</td>\n", |
||
| 2709 | " <td>0.0</td>\n", |
||
| 2710 | " <td>0.0</td>\n", |
||
| 2711 | " <td>0.0</td>\n", |
||
| 2712 | " <td>0.0</td>\n", |
||
| 2713 | " <td>0.0</td>\n", |
||
| 2714 | " <td>...</td>\n", |
||
| 2715 | " <td>-1.000000</td>\n", |
||
| 2716 | " <td>-1.000000</td>\n", |
||
| 2717 | " <td>-1.000000</td>\n", |
||
| 2718 | " <td>-1.000000</td>\n", |
||
| 2719 | " <td>-1.000000</td>\n", |
||
| 2720 | " <td>-1.000000</td>\n", |
||
| 2721 | " <td>-1.000000</td>\n", |
||
| 2722 | " <td>1</td>\n", |
||
| 2723 | " <td>1</td>\n", |
||
| 2724 | " <td>1</td>\n", |
||
| 2725 | " </tr>\n", |
||
| 2726 | " <tr>\n", |
||
| 2727 | " <th>7</th>\n", |
||
| 2728 | " <td>1</td>\n", |
||
| 2729 | " <td>1</td>\n", |
||
| 2730 | " <td>8</td>\n", |
||
| 2731 | " <td>1</td>\n", |
||
| 2732 | " <td>0.0</td>\n", |
||
| 2733 | " <td>0.0</td>\n", |
||
| 2734 | " <td>0.0</td>\n", |
||
| 2735 | " <td>0.0</td>\n", |
||
| 2736 | " <td>0.0</td>\n", |
||
| 2737 | " <td>0.0</td>\n", |
||
| 2738 | " <td>...</td>\n", |
||
| 2739 | " <td>-1.000000</td>\n", |
||
| 2740 | " <td>-1.000000</td>\n", |
||
| 2741 | " <td>-1.000000</td>\n", |
||
| 2742 | " <td>-1.000000</td>\n", |
||
| 2743 | " <td>-1.000000</td>\n", |
||
| 2744 | " <td>-1.000000</td>\n", |
||
| 2745 | " <td>-1.000000</td>\n", |
||
| 2746 | " <td>2</td>\n", |
||
| 2747 | " <td>1</td>\n", |
||
| 2748 | " <td>1</td>\n", |
||
| 2749 | " </tr>\n", |
||
| 2750 | " <tr>\n", |
||
| 2751 | " <th>8</th>\n", |
||
| 2752 | " <td>1</td>\n", |
||
| 2753 | " <td>1</td>\n", |
||
| 2754 | " <td>8</td>\n", |
||
| 2755 | " <td>1</td>\n", |
||
| 2756 | " <td>0.0</td>\n", |
||
| 2757 | " <td>0.0</td>\n", |
||
| 2758 | " <td>0.0</td>\n", |
||
| 2759 | " <td>0.0</td>\n", |
||
| 2760 | " <td>0.0</td>\n", |
||
| 2761 | " <td>0.0</td>\n", |
||
| 2762 | " <td>...</td>\n", |
||
| 2763 | " <td>-1.000000</td>\n", |
||
| 2764 | " <td>-0.906832</td>\n", |
||
| 2765 | " <td>-0.831132</td>\n", |
||
| 2766 | " <td>-0.940967</td>\n", |
||
| 2767 | " <td>-0.817204</td>\n", |
||
| 2768 | " <td>-0.882574</td>\n", |
||
| 2769 | " <td>-1.000000</td>\n", |
||
| 2770 | " <td>3</td>\n", |
||
| 2771 | " <td>1</td>\n", |
||
| 2772 | " <td>1</td>\n", |
||
| 2773 | " </tr>\n", |
||
| 2774 | " <tr>\n", |
||
| 2775 | " <th>9</th>\n", |
||
| 2776 | " <td>1</td>\n", |
||
| 2777 | " <td>1</td>\n", |
||
| 2778 | " <td>8</td>\n", |
||
| 2779 | " <td>1</td>\n", |
||
| 2780 | " <td>0.0</td>\n", |
||
| 2781 | " <td>0.0</td>\n", |
||
| 2782 | " <td>0.0</td>\n", |
||
| 2783 | " <td>0.0</td>\n", |
||
| 2784 | " <td>1.0</td>\n", |
||
| 2785 | " <td>0.0</td>\n", |
||
| 2786 | " <td>...</td>\n", |
||
| 2787 | " <td>-0.797980</td>\n", |
||
| 2788 | " <td>0.315690</td>\n", |
||
| 2789 | " <td>0.200359</td>\n", |
||
| 2790 | " <td>-0.239515</td>\n", |
||
| 2791 | " <td>0.645161</td>\n", |
||
| 2792 | " <td>0.139709</td>\n", |
||
| 2793 | " <td>-0.802317</td>\n", |
||
| 2794 | " <td>4</td>\n", |
||
| 2795 | " <td>1</td>\n", |
||
| 2796 | " <td>1</td>\n", |
||
| 2797 | " </tr>\n", |
||
| 2798 | " <tr>\n", |
||
| 2799 | " <th>10</th>\n", |
||
| 2800 | " <td>2</td>\n", |
||
| 2801 | " <td>0</td>\n", |
||
| 2802 | " <td>0</td>\n", |
||
| 2803 | " <td>0</td>\n", |
||
| 2804 | " <td>0.0</td>\n", |
||
| 2805 | " <td>0.0</td>\n", |
||
| 2806 | " <td>0.0</td>\n", |
||
| 2807 | " <td>0.0</td>\n", |
||
| 2808 | " <td>0.0</td>\n", |
||
| 2809 | " <td>0.0</td>\n", |
||
| 2810 | " <td>...</td>\n", |
||
| 2811 | " <td>-0.959596</td>\n", |
||
| 2812 | " <td>-0.515528</td>\n", |
||
| 2813 | " <td>-0.351328</td>\n", |
||
| 2814 | " <td>-0.747001</td>\n", |
||
| 2815 | " <td>-0.756272</td>\n", |
||
| 2816 | " <td>-1.000000</td>\n", |
||
| 2817 | " <td>-0.961262</td>\n", |
||
| 2818 | " <td>0</td>\n", |
||
| 2819 | " <td>0</td>\n", |
||
| 2820 | " <td>1</td>\n", |
||
| 2821 | " </tr>\n", |
||
| 2822 | " <tr>\n", |
||
| 2823 | " <th>11</th>\n", |
||
| 2824 | " <td>2</td>\n", |
||
| 2825 | " <td>0</td>\n", |
||
| 2826 | " <td>0</td>\n", |
||
| 2827 | " <td>0</td>\n", |
||
| 2828 | " <td>0.0</td>\n", |
||
| 2829 | " <td>0.0</td>\n", |
||
| 2830 | " <td>0.0</td>\n", |
||
| 2831 | " <td>0.0</td>\n", |
||
| 2832 | " <td>0.0</td>\n", |
||
| 2833 | " <td>0.0</td>\n", |
||
| 2834 | " <td>...</td>\n", |
||
| 2835 | " <td>-0.959596</td>\n", |
||
| 2836 | " <td>-0.515528</td>\n", |
||
| 2837 | " <td>-0.351328</td>\n", |
||
| 2838 | " <td>-0.747001</td>\n", |
||
| 2839 | " <td>-0.756272</td>\n", |
||
| 2840 | " <td>-1.000000</td>\n", |
||
| 2841 | " <td>-0.961262</td>\n", |
||
| 2842 | " <td>1</td>\n", |
||
| 2843 | " <td>0</td>\n", |
||
| 2844 | " <td>1</td>\n", |
||
| 2845 | " </tr>\n", |
||
| 2846 | " <tr>\n", |
||
| 2847 | " <th>12</th>\n", |
||
| 2848 | " <td>2</td>\n", |
||
| 2849 | " <td>0</td>\n", |
||
| 2850 | " <td>0</td>\n", |
||
| 2851 | " <td>0</td>\n", |
||
| 2852 | " <td>0.0</td>\n", |
||
| 2853 | " <td>0.0</td>\n", |
||
| 2854 | " <td>0.0</td>\n", |
||
| 2855 | " <td>0.0</td>\n", |
||
| 2856 | " <td>0.0</td>\n", |
||
| 2857 | " <td>0.0</td>\n", |
||
| 2858 | " <td>...</td>\n", |
||
| 2859 | " <td>-0.959596</td>\n", |
||
| 2860 | " <td>-0.515528</td>\n", |
||
| 2861 | " <td>-0.351328</td>\n", |
||
| 2862 | " <td>-0.747001</td>\n", |
||
| 2863 | " <td>-0.756272</td>\n", |
||
| 2864 | " <td>-1.000000</td>\n", |
||
| 2865 | " <td>-0.961262</td>\n", |
||
| 2866 | " <td>2</td>\n", |
||
| 2867 | " <td>0</td>\n", |
||
| 2868 | " <td>1</td>\n", |
||
| 2869 | " </tr>\n", |
||
| 2870 | " <tr>\n", |
||
| 2871 | " <th>13</th>\n", |
||
| 2872 | " <td>2</td>\n", |
||
| 2873 | " <td>0</td>\n", |
||
| 2874 | " <td>0</td>\n", |
||
| 2875 | " <td>0</td>\n", |
||
| 2876 | " <td>0.0</td>\n", |
||
| 2877 | " <td>0.0</td>\n", |
||
| 2878 | " <td>0.0</td>\n", |
||
| 2879 | " <td>0.0</td>\n", |
||
| 2880 | " <td>0.0</td>\n", |
||
| 2881 | " <td>0.0</td>\n", |
||
| 2882 | " <td>...</td>\n", |
||
| 2883 | " <td>-0.797980</td>\n", |
||
| 2884 | " <td>-0.658863</td>\n", |
||
| 2885 | " <td>-0.563758</td>\n", |
||
| 2886 | " <td>-0.721834</td>\n", |
||
| 2887 | " <td>-0.926882</td>\n", |
||
| 2888 | " <td>-1.000000</td>\n", |
||
| 2889 | " <td>-0.801293</td>\n", |
||
| 2890 | " <td>3</td>\n", |
||
| 2891 | " <td>0</td>\n", |
||
| 2892 | " <td>1</td>\n", |
||
| 2893 | " </tr>\n", |
||
| 2894 | " <tr>\n", |
||
| 2895 | " <th>14</th>\n", |
||
| 2896 | " <td>2</td>\n", |
||
| 2897 | " <td>0</td>\n", |
||
| 2898 | " <td>0</td>\n", |
||
| 2899 | " <td>0</td>\n", |
||
| 2900 | " <td>0.0</td>\n", |
||
| 2901 | " <td>0.0</td>\n", |
||
| 2902 | " <td>0.0</td>\n", |
||
| 2903 | " <td>0.0</td>\n", |
||
| 2904 | " <td>0.0</td>\n", |
||
| 2905 | " <td>0.0</td>\n", |
||
| 2906 | " <td>...</td>\n", |
||
| 2907 | " <td>-0.898990</td>\n", |
||
| 2908 | " <td>-0.612422</td>\n", |
||
| 2909 | " <td>-0.343258</td>\n", |
||
| 2910 | " <td>-0.576744</td>\n", |
||
| 2911 | " <td>-0.695341</td>\n", |
||
| 2912 | " <td>-0.505464</td>\n", |
||
| 2913 | " <td>-0.900129</td>\n", |
||
| 2914 | " <td>4</td>\n", |
||
| 2915 | " <td>1</td>\n", |
||
| 2916 | " <td>1</td>\n", |
||
| 2917 | " </tr>\n", |
||
| 2918 | " <tr>\n", |
||
| 2919 | " <th>15</th>\n", |
||
| 2920 | " <td>3</td>\n", |
||
| 2921 | " <td>0</td>\n", |
||
| 2922 | " <td>3</td>\n", |
||
| 2923 | " <td>1</td>\n", |
||
| 2924 | " <td>0.0</td>\n", |
||
| 2925 | " <td>0.0</td>\n", |
||
| 2926 | " <td>0.0</td>\n", |
||
| 2927 | " <td>0.0</td>\n", |
||
| 2928 | " <td>0.0</td>\n", |
||
| 2929 | " <td>0.0</td>\n", |
||
| 2930 | " <td>...</td>\n", |
||
| 2931 | " <td>-1.000000</td>\n", |
||
| 2932 | " <td>-1.000000</td>\n", |
||
| 2933 | " <td>-1.000000</td>\n", |
||
| 2934 | " <td>-1.000000</td>\n", |
||
| 2935 | " <td>-1.000000</td>\n", |
||
| 2936 | " <td>-1.000000</td>\n", |
||
| 2937 | " <td>-1.000000</td>\n", |
||
| 2938 | " <td>0</td>\n", |
||
| 2939 | " <td>0</td>\n", |
||
| 2940 | " <td>0</td>\n", |
||
| 2941 | " </tr>\n", |
||
| 2942 | " <tr>\n", |
||
| 2943 | " <th>16</th>\n", |
||
| 2944 | " <td>3</td>\n", |
||
| 2945 | " <td>0</td>\n", |
||
| 2946 | " <td>3</td>\n", |
||
| 2947 | " <td>1</td>\n", |
||
| 2948 | " <td>0.0</td>\n", |
||
| 2949 | " <td>0.0</td>\n", |
||
| 2950 | " <td>0.0</td>\n", |
||
| 2951 | " <td>0.0</td>\n", |
||
| 2952 | " <td>0.0</td>\n", |
||
| 2953 | " <td>0.0</td>\n", |
||
| 2954 | " <td>...</td>\n", |
||
| 2955 | " <td>-1.000000</td>\n", |
||
| 2956 | " <td>-1.000000</td>\n", |
||
| 2957 | " <td>-1.000000</td>\n", |
||
| 2958 | " <td>-1.000000</td>\n", |
||
| 2959 | " <td>-1.000000</td>\n", |
||
| 2960 | " <td>-1.000000</td>\n", |
||
| 2961 | " <td>-1.000000</td>\n", |
||
| 2962 | " <td>1</td>\n", |
||
| 2963 | " <td>0</td>\n", |
||
| 2964 | " <td>0</td>\n", |
||
| 2965 | " </tr>\n", |
||
| 2966 | " <tr>\n", |
||
| 2967 | " <th>17</th>\n", |
||
| 2968 | " <td>3</td>\n", |
||
| 2969 | " <td>0</td>\n", |
||
| 2970 | " <td>3</td>\n", |
||
| 2971 | " <td>1</td>\n", |
||
| 2972 | " <td>0.0</td>\n", |
||
| 2973 | " <td>0.0</td>\n", |
||
| 2974 | " <td>0.0</td>\n", |
||
| 2975 | " <td>0.0</td>\n", |
||
| 2976 | " <td>0.0</td>\n", |
||
| 2977 | " <td>0.0</td>\n", |
||
| 2978 | " <td>...</td>\n", |
||
| 2979 | " <td>-1.000000</td>\n", |
||
| 2980 | " <td>-1.000000</td>\n", |
||
| 2981 | " <td>-1.000000</td>\n", |
||
| 2982 | " <td>-1.000000</td>\n", |
||
| 2983 | " <td>-1.000000</td>\n", |
||
| 2984 | " <td>-1.000000</td>\n", |
||
| 2985 | " <td>-1.000000</td>\n", |
||
| 2986 | " <td>2</td>\n", |
||
| 2987 | " <td>0</td>\n", |
||
| 2988 | " <td>0</td>\n", |
||
| 2989 | " </tr>\n", |
||
| 2990 | " <tr>\n", |
||
| 2991 | " <th>18</th>\n", |
||
| 2992 | " <td>3</td>\n", |
||
| 2993 | " <td>0</td>\n", |
||
| 2994 | " <td>3</td>\n", |
||
| 2995 | " <td>1</td>\n", |
||
| 2996 | " <td>0.0</td>\n", |
||
| 2997 | " <td>0.0</td>\n", |
||
| 2998 | " <td>0.0</td>\n", |
||
| 2999 | " <td>0.0</td>\n", |
||
| 3000 | " <td>0.0</td>\n", |
||
| 3001 | " <td>0.0</td>\n", |
||
| 3002 | " <td>...</td>\n", |
||
| 3003 | " <td>-1.000000</td>\n", |
||
| 3004 | " <td>-1.000000</td>\n", |
||
| 3005 | " <td>-1.000000</td>\n", |
||
| 3006 | " <td>-1.000000</td>\n", |
||
| 3007 | " <td>-1.000000</td>\n", |
||
| 3008 | " <td>-1.000000</td>\n", |
||
| 3009 | " <td>-1.000000</td>\n", |
||
| 3010 | " <td>3</td>\n", |
||
| 3011 | " <td>0</td>\n", |
||
| 3012 | " <td>0</td>\n", |
||
| 3013 | " </tr>\n", |
||
| 3014 | " <tr>\n", |
||
| 3015 | " <th>19</th>\n", |
||
| 3016 | " <td>3</td>\n", |
||
| 3017 | " <td>0</td>\n", |
||
| 3018 | " <td>3</td>\n", |
||
| 3019 | " <td>1</td>\n", |
||
| 3020 | " <td>0.0</td>\n", |
||
| 3021 | " <td>0.0</td>\n", |
||
| 3022 | " <td>0.0</td>\n", |
||
| 3023 | " <td>0.0</td>\n", |
||
| 3024 | " <td>0.0</td>\n", |
||
| 3025 | " <td>0.0</td>\n", |
||
| 3026 | " <td>...</td>\n", |
||
| 3027 | " <td>-0.171717</td>\n", |
||
| 3028 | " <td>-0.308696</td>\n", |
||
| 3029 | " <td>-0.057718</td>\n", |
||
| 3030 | " <td>-0.069094</td>\n", |
||
| 3031 | " <td>-0.329749</td>\n", |
||
| 3032 | " <td>-0.047619</td>\n", |
||
| 3033 | " <td>-0.172436</td>\n", |
||
| 3034 | " <td>4</td>\n", |
||
| 3035 | " <td>0</td>\n", |
||
| 3036 | " <td>0</td>\n", |
||
| 3037 | " </tr>\n", |
||
| 3038 | " <tr>\n", |
||
| 3039 | " <th>20</th>\n", |
||
| 3040 | " <td>4</td>\n", |
||
| 3041 | " <td>0</td>\n", |
||
| 3042 | " <td>0</td>\n", |
||
| 3043 | " <td>0</td>\n", |
||
| 3044 | " <td>0.0</td>\n", |
||
| 3045 | " <td>0.0</td>\n", |
||
| 3046 | " <td>0.0</td>\n", |
||
| 3047 | " <td>0.0</td>\n", |
||
| 3048 | " <td>0.0</td>\n", |
||
| 3049 | " <td>0.0</td>\n", |
||
| 3050 | " <td>...</td>\n", |
||
| 3051 | " <td>-0.979798</td>\n", |
||
| 3052 | " <td>-1.000000</td>\n", |
||
| 3053 | " <td>-0.883669</td>\n", |
||
| 3054 | " <td>-0.956805</td>\n", |
||
| 3055 | " <td>-0.870968</td>\n", |
||
| 3056 | " <td>-0.953536</td>\n", |
||
| 3057 | " <td>-0.980333</td>\n", |
||
| 3058 | " <td>0</td>\n", |
||
| 3059 | " <td>0</td>\n", |
||
| 3060 | " <td>0</td>\n", |
||
| 3061 | " </tr>\n", |
||
| 3062 | " <tr>\n", |
||
| 3063 | " <th>21</th>\n", |
||
| 3064 | " <td>4</td>\n", |
||
| 3065 | " <td>0</td>\n", |
||
| 3066 | " <td>0</td>\n", |
||
| 3067 | " <td>0</td>\n", |
||
| 3068 | " <td>0.0</td>\n", |
||
| 3069 | " <td>0.0</td>\n", |
||
| 3070 | " <td>0.0</td>\n", |
||
| 3071 | " <td>0.0</td>\n", |
||
| 3072 | " <td>0.0</td>\n", |
||
| 3073 | " <td>0.0</td>\n", |
||
| 3074 | " <td>...</td>\n", |
||
| 3075 | " <td>-0.979798</td>\n", |
||
| 3076 | " <td>-1.000000</td>\n", |
||
| 3077 | " <td>-0.883669</td>\n", |
||
| 3078 | " <td>-0.956805</td>\n", |
||
| 3079 | " <td>-0.870968</td>\n", |
||
| 3080 | " <td>-0.953536</td>\n", |
||
| 3081 | " <td>-0.980333</td>\n", |
||
| 3082 | " <td>1</td>\n", |
||
| 3083 | " <td>0</td>\n", |
||
| 3084 | " <td>0</td>\n", |
||
| 3085 | " </tr>\n", |
||
| 3086 | " <tr>\n", |
||
| 3087 | " <th>22</th>\n", |
||
| 3088 | " <td>4</td>\n", |
||
| 3089 | " <td>0</td>\n", |
||
| 3090 | " <td>0</td>\n", |
||
| 3091 | " <td>0</td>\n", |
||
| 3092 | " <td>0.0</td>\n", |
||
| 3093 | " <td>0.0</td>\n", |
||
| 3094 | " <td>0.0</td>\n", |
||
| 3095 | " <td>0.0</td>\n", |
||
| 3096 | " <td>0.0</td>\n", |
||
| 3097 | " <td>0.0</td>\n", |
||
| 3098 | " <td>...</td>\n", |
||
| 3099 | " <td>-0.979798</td>\n", |
||
| 3100 | " <td>-1.000000</td>\n", |
||
| 3101 | " <td>-0.883669</td>\n", |
||
| 3102 | " <td>-0.956805</td>\n", |
||
| 3103 | " <td>-0.870968</td>\n", |
||
| 3104 | " <td>-0.953536</td>\n", |
||
| 3105 | " <td>-0.980333</td>\n", |
||
| 3106 | " <td>2</td>\n", |
||
| 3107 | " <td>0</td>\n", |
||
| 3108 | " <td>0</td>\n", |
||
| 3109 | " </tr>\n", |
||
| 3110 | " <tr>\n", |
||
| 3111 | " <th>23</th>\n", |
||
| 3112 | " <td>4</td>\n", |
||
| 3113 | " <td>0</td>\n", |
||
| 3114 | " <td>0</td>\n", |
||
| 3115 | " <td>0</td>\n", |
||
| 3116 | " <td>0.0</td>\n", |
||
| 3117 | " <td>0.0</td>\n", |
||
| 3118 | " <td>0.0</td>\n", |
||
| 3119 | " <td>0.0</td>\n", |
||
| 3120 | " <td>0.0</td>\n", |
||
| 3121 | " <td>0.0</td>\n", |
||
| 3122 | " <td>...</td>\n", |
||
| 3123 | " <td>-0.959596</td>\n", |
||
| 3124 | " <td>-1.000000</td>\n", |
||
| 3125 | " <td>-1.000000</td>\n", |
||
| 3126 | " <td>-0.926209</td>\n", |
||
| 3127 | " <td>-1.000000</td>\n", |
||
| 3128 | " <td>-0.698797</td>\n", |
||
| 3129 | " <td>-0.960463</td>\n", |
||
| 3130 | " <td>3</td>\n", |
||
| 3131 | " <td>0</td>\n", |
||
| 3132 | " <td>0</td>\n", |
||
| 3133 | " </tr>\n", |
||
| 3134 | " <tr>\n", |
||
| 3135 | " <th>24</th>\n", |
||
| 3136 | " <td>4</td>\n", |
||
| 3137 | " <td>0</td>\n", |
||
| 3138 | " <td>0</td>\n", |
||
| 3139 | " <td>0</td>\n", |
||
| 3140 | " <td>0.0</td>\n", |
||
| 3141 | " <td>0.0</td>\n", |
||
| 3142 | " <td>0.0</td>\n", |
||
| 3143 | " <td>0.0</td>\n", |
||
| 3144 | " <td>0.0</td>\n", |
||
| 3145 | " <td>0.0</td>\n", |
||
| 3146 | " <td>...</td>\n", |
||
| 3147 | " <td>-0.939394</td>\n", |
||
| 3148 | " <td>-0.652174</td>\n", |
||
| 3149 | " <td>-0.596165</td>\n", |
||
| 3150 | " <td>-0.634847</td>\n", |
||
| 3151 | " <td>-0.817204</td>\n", |
||
| 3152 | " <td>-0.645793</td>\n", |
||
| 3153 | " <td>-0.940077</td>\n", |
||
| 3154 | " <td>4</td>\n", |
||
| 3155 | " <td>0</td>\n", |
||
| 3156 | " <td>0</td>\n", |
||
| 3157 | " </tr>\n", |
||
| 3158 | " </tbody>\n", |
||
| 3159 | "</table>\n", |
||
| 3160 | "<p>25 rows × 232 columns</p>\n", |
||
| 3161 | "</div>\n", |
||
| 3162 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62c5ddb9-3675-4f84-b74d-a64dc4b552ae')\"\n", |
||
| 3163 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 3164 | " style=\"display:none;\">\n", |
||
| 3165 | " \n", |
||
| 3166 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 3167 | " width=\"24px\">\n", |
||
| 3168 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 3169 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 3170 | " </svg>\n", |
||
| 3171 | " </button>\n", |
||
| 3172 | " \n", |
||
| 3173 | " <style>\n", |
||
| 3174 | " .colab-df-container {\n", |
||
| 3175 | " display:flex;\n", |
||
| 3176 | " flex-wrap:wrap;\n", |
||
| 3177 | " gap: 12px;\n", |
||
| 3178 | " }\n", |
||
| 3179 | "\n", |
||
| 3180 | " .colab-df-convert {\n", |
||
| 3181 | " background-color: #E8F0FE;\n", |
||
| 3182 | " border: none;\n", |
||
| 3183 | " border-radius: 50%;\n", |
||
| 3184 | " cursor: pointer;\n", |
||
| 3185 | " display: none;\n", |
||
| 3186 | " fill: #1967D2;\n", |
||
| 3187 | " height: 32px;\n", |
||
| 3188 | " padding: 0 0 0 0;\n", |
||
| 3189 | " width: 32px;\n", |
||
| 3190 | " }\n", |
||
| 3191 | "\n", |
||
| 3192 | " .colab-df-convert:hover {\n", |
||
| 3193 | " background-color: #E2EBFA;\n", |
||
| 3194 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 3195 | " fill: #174EA6;\n", |
||
| 3196 | " }\n", |
||
| 3197 | "\n", |
||
| 3198 | " [theme=dark] .colab-df-convert {\n", |
||
| 3199 | " background-color: #3B4455;\n", |
||
| 3200 | " fill: #D2E3FC;\n", |
||
| 3201 | " }\n", |
||
| 3202 | "\n", |
||
| 3203 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 3204 | " background-color: #434B5C;\n", |
||
| 3205 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 3206 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 3207 | " fill: #FFFFFF;\n", |
||
| 3208 | " }\n", |
||
| 3209 | " </style>\n", |
||
| 3210 | "\n", |
||
| 3211 | " <script>\n", |
||
| 3212 | " const buttonEl =\n", |
||
| 3213 | " document.querySelector('#df-62c5ddb9-3675-4f84-b74d-a64dc4b552ae button.colab-df-convert');\n", |
||
| 3214 | " buttonEl.style.display =\n", |
||
| 3215 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 3216 | "\n", |
||
| 3217 | " async function convertToInteractive(key) {\n", |
||
| 3218 | " const element = document.querySelector('#df-62c5ddb9-3675-4f84-b74d-a64dc4b552ae');\n", |
||
| 3219 | " const dataTable =\n", |
||
| 3220 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 3221 | " [key], {});\n", |
||
| 3222 | " if (!dataTable) return;\n", |
||
| 3223 | "\n", |
||
| 3224 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 3225 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 3226 | " + ' to learn more about interactive tables.';\n", |
||
| 3227 | " element.innerHTML = '';\n", |
||
| 3228 | " dataTable['output_type'] = 'display_data';\n", |
||
| 3229 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 3230 | " const docLink = document.createElement('div');\n", |
||
| 3231 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 3232 | " element.appendChild(docLink);\n", |
||
| 3233 | " }\n", |
||
| 3234 | " </script>\n", |
||
| 3235 | " </div>\n", |
||
| 3236 | " </div>\n", |
||
| 3237 | " " |
||
| 3238 | ] |
||
| 3239 | }, |
||
| 3240 | "metadata": {}, |
||
| 3241 | "execution_count": 23 |
||
| 3242 | } |
||
| 3243 | ] |
||
| 3244 | }, |
||
| 3245 | { |
||
| 3246 | "cell_type": "code", |
||
| 3247 | "source": [ |
||
| 3248 | "# Check for null data\n", |
||
| 3249 | "filled_data.isnull().values.any()" |
||
| 3250 | ], |
||
| 3251 | "metadata": { |
||
| 3252 | "id": "g1j5PVftEUBh", |
||
| 3253 | "colab": { |
||
| 3254 | "base_uri": "https://localhost:8080/" |
||
| 3255 | }, |
||
| 3256 | "outputId": "ddfb51d7-c152-4ca2-bb58-b97477100ee2" |
||
| 3257 | }, |
||
| 3258 | "execution_count": 24, |
||
| 3259 | "outputs": [ |
||
| 3260 | { |
||
| 3261 | "output_type": "execute_result", |
||
| 3262 | "data": { |
||
| 3263 | "text/plain": [ |
||
| 3264 | "False" |
||
| 3265 | ] |
||
| 3266 | }, |
||
| 3267 | "metadata": {}, |
||
| 3268 | "execution_count": 24 |
||
| 3269 | } |
||
| 3270 | ] |
||
| 3271 | }, |
||
| 3272 | { |
||
| 3273 | "cell_type": "code", |
||
| 3274 | "source": [ |
||
| 3275 | "# Check for duplicates\n", |
||
| 3276 | "filled_data.duplicated().values.any()" |
||
| 3277 | ], |
||
| 3278 | "metadata": { |
||
| 3279 | "id": "I9Gc_r76y5xw", |
||
| 3280 | "colab": { |
||
| 3281 | "base_uri": "https://localhost:8080/" |
||
| 3282 | }, |
||
| 3283 | "outputId": "0350f5ce-9ebe-4764-a16f-aa7028c4c3ee" |
||
| 3284 | }, |
||
| 3285 | "execution_count": 25, |
||
| 3286 | "outputs": [ |
||
| 3287 | { |
||
| 3288 | "output_type": "execute_result", |
||
| 3289 | "data": { |
||
| 3290 | "text/plain": [ |
||
| 3291 | "False" |
||
| 3292 | ] |
||
| 3293 | }, |
||
| 3294 | "metadata": {}, |
||
| 3295 | "execution_count": 25 |
||
| 3296 | } |
||
| 3297 | ] |
||
| 3298 | }, |
||
| 3299 | { |
||
| 3300 | "cell_type": "markdown", |
||
| 3301 | "source": [ |
||
| 3302 | "# Machine Learning Models" |
||
| 3303 | ], |
||
| 3304 | "metadata": { |
||
| 3305 | "id": "RzUte5IYxe5F" |
||
| 3306 | } |
||
| 3307 | }, |
||
| 3308 | { |
||
| 3309 | "cell_type": "code", |
||
| 3310 | "source": [ |
||
| 3311 | "# Set x and y values for machine learning models; y is the target variable - patients admitted into ICU.\n", |
||
| 3312 | "X_data = np.array(filled_data.drop(['ICU'], axis = 1))\n", |
||
| 3313 | "Y_data = np.array(filled_data[['ICU']])" |
||
| 3314 | ], |
||
| 3315 | "metadata": { |
||
| 3316 | "id": "qYUccyZjMQ0S" |
||
| 3317 | }, |
||
| 3318 | "execution_count": 26, |
||
| 3319 | "outputs": [] |
||
| 3320 | }, |
||
| 3321 | { |
||
| 3322 | "cell_type": "markdown", |
||
| 3323 | "source": [ |
||
| 3324 | "*For all machine learning models*:\n", |
||
| 3325 | "\n", |
||
| 3326 | "We want the algorithm to receive historical data to learn patterns and also to test the learned patterns, so the database will be split into training data to fit/\"teach\" the algorithm and test data to test its effectiveness.\n", |
||
| 3327 | "\n", |
||
| 3328 | "The test sample will contain 30% of the database.\n", |
||
| 3329 | "\n", |
||
| 3330 | "If the number of patients going into ICU on the training data is very different from the test data, the model may not be trained properly. To balance the division, use the stratify parameter.\n", |
||
| 3331 | "\n", |
||
| 3332 | "<!-- The models will be trained and tested with all data from the database which does not allow for making predictions on future ICU admissions, but we will use it as a benchmark to compare the models when run with modified/cleaned data. -->" |
||
| 3333 | ], |
||
| 3334 | "metadata": { |
||
| 3335 | "id": "QvUmzmKcjVse" |
||
| 3336 | } |
||
| 3337 | }, |
||
| 3338 | { |
||
| 3339 | "cell_type": "markdown", |
||
| 3340 | "source": [ |
||
| 3341 | "**Logistic Regression Model**\n", |
||
| 3342 | "\n", |
||
| 3343 | "Logistic regression predicts the output of a categorical dependent variable. Therefore the outcome shall be a categorical or discrete value. It can be either Yes or No, 0 or 1, True or False etc; this makes it a binary classifier.\n", |
||
| 3344 | "\n", |
||
| 3345 | "The algorithm, however, gives the probability that an instance belongs to a particular class instead of giving the exact value as 0 and 1. For this database, logistic regression will return the probability of each patient going into ICU. If the estimated probability is greater than 50%, then the model predicts that the patient will go into ICU." |
||
| 3346 | ], |
||
| 3347 | "metadata": { |
||
| 3348 | "id": "2f07qJ0QIT8s" |
||
| 3349 | } |
||
| 3350 | }, |
||
| 3351 | { |
||
| 3352 | "cell_type": "code", |
||
| 3353 | "source": [ |
||
| 3354 | "def log_reg_model(df):\n", |
||
| 3355 | " df_test = df.copy()\n", |
||
| 3356 | " target = 'ICU'\n", |
||
| 3357 | " y = df_test.pop(target)\n", |
||
| 3358 | " X = df_test\n", |
||
| 3359 | " X_train, X_test, y_train, y_test = train_test_split(X_data, Y_data, test_size = 0.30, stratify = Y_data)\n", |
||
| 3360 | "\n", |
||
| 3361 | " print(f'Training data: {X_train.shape}')\n", |
||
| 3362 | " print(f'Test data: {X_test.shape}\\n')\n", |
||
| 3363 | "\n", |
||
| 3364 | " lr_model = LogisticRegression(max_iter = 10000, solver='lbfgs')\n", |
||
| 3365 | " lr_model.fit(X_train, y_train)\n", |
||
| 3366 | " y_test_pred = lr_model.predict(X_test)\n", |
||
| 3367 | "\n", |
||
| 3368 | " print(metrics.classification_report(y_test, y_test_pred))\n", |
||
| 3369 | "\n", |
||
| 3370 | " print(\"ROC_AUC_Score:\",roc_auc_score(y_test, y_test_pred))\n", |
||
| 3371 | "\n", |
||
| 3372 | " plt.figure(figsize = (7, 5))\n", |
||
| 3373 | " sns.heatmap(confusion_matrix(y_test, y_test_pred), annot = True, cmap = 'CMRmap_r', fmt = \"d\", cbar = True, xticklabels = ['No', 'Yes'], yticklabels = ['No', 'Yes'], annot_kws = {\"fontsize\":15})\n", |
||
| 3374 | " plt.show()\n", |
||
| 3375 | "\n", |
||
| 3376 | " return" |
||
| 3377 | ], |
||
| 3378 | "metadata": { |
||
| 3379 | "id": "8wqBTglbycKr" |
||
| 3380 | }, |
||
| 3381 | "execution_count": 27, |
||
| 3382 | "outputs": [] |
||
| 3383 | }, |
||
| 3384 | { |
||
| 3385 | "cell_type": "code", |
||
| 3386 | "source": [ |
||
| 3387 | "# score_all = log_reg_model(filled_data)" |
||
| 3388 | ], |
||
| 3389 | "metadata": { |
||
| 3390 | "id": "kQGCbVzm0y5T" |
||
| 3391 | }, |
||
| 3392 | "execution_count": null, |
||
| 3393 | "outputs": [] |
||
| 3394 | }, |
||
| 3395 | { |
||
| 3396 | "cell_type": "markdown", |
||
| 3397 | "source": [ |
||
| 3398 | "**Decision Tree Model**\n", |
||
| 3399 | "\n", |
||
| 3400 | "Decision trees are versatile machine learning algorithms that can perform both classification and regression tasks, and even multioutput tasks. They are capable of fitting complex datasets. In a decision tree, there are two nodes, which are the decision node and leaf node; the first ones are used to make any decision and may result in other branches, while the leaf nodes are the output of those decisions and do not contain any further branches." |
||
| 3401 | ], |
||
| 3402 | "metadata": { |
||
| 3403 | "id": "qa2QBmWtFK9v" |
||
| 3404 | } |
||
| 3405 | }, |
||
| 3406 | { |
||
| 3407 | "cell_type": "code", |
||
| 3408 | "source": [ |
||
| 3409 | "def decision_tree_model(df):\n", |
||
| 3410 | " df_test = df.copy()\n", |
||
| 3411 | " target = 'ICU'\n", |
||
| 3412 | " y = df_test.pop(target)\n", |
||
| 3413 | " X = df_test\n", |
||
| 3414 | " X_train, X_test, y_train, y_test = train_test_split(X_data, Y_data, test_size = 0.30, stratify = Y_data)\n", |
||
| 3415 | "\n", |
||
| 3416 | " print(f'Training data: {X_train.shape}')\n", |
||
| 3417 | " print(f'Test data: {X_test.shape}\\n')\n", |
||
| 3418 | "\n", |
||
| 3419 | " dt_model = tree.DecisionTreeClassifier(max_depth=8) #criterion='entropy',,max_leaf_nodes=10\n", |
||
| 3420 | " dt_model.fit(X_train,y_train)\n", |
||
| 3421 | " y_test_pred = dt_model.predict(X_test)\n", |
||
| 3422 | "\n", |
||
| 3423 | " print(metrics.classification_report(y_test, y_test_pred))\n", |
||
| 3424 | "\n", |
||
| 3425 | " print(\"ROC_AUC_Score:\",roc_auc_score(y_test, y_test_pred))\n", |
||
| 3426 | "\n", |
||
| 3427 | " plt.figure(figsize = (7, 5))\n", |
||
| 3428 | " sns.heatmap(confusion_matrix(y_test, y_test_pred), annot = True, cmap = 'CMRmap_r', fmt = \"d\", cbar = True, xticklabels = ['No', 'Yes'], yticklabels = ['No', 'Yes'], annot_kws = {\"fontsize\":15})\n", |
||
| 3429 | " plt.show()\n", |
||
| 3430 | "\n", |
||
| 3431 | " return" |
||
| 3432 | ], |
||
| 3433 | "metadata": { |
||
| 3434 | "id": "CQ7OxG2b_FQ9" |
||
| 3435 | }, |
||
| 3436 | "execution_count": 28, |
||
| 3437 | "outputs": [] |
||
| 3438 | }, |
||
| 3439 | { |
||
| 3440 | "cell_type": "code", |
||
| 3441 | "source": [ |
||
| 3442 | "# score_all = decision_tree_model(filled_data)" |
||
| 3443 | ], |
||
| 3444 | "metadata": { |
||
| 3445 | "id": "7_rJhCDq_xoe" |
||
| 3446 | }, |
||
| 3447 | "execution_count": null, |
||
| 3448 | "outputs": [] |
||
| 3449 | }, |
||
| 3450 | { |
||
| 3451 | "cell_type": "markdown", |
||
| 3452 | "source": [ |
||
| 3453 | "**K-Neighbors Model**\n", |
||
| 3454 | "\n", |
||
| 3455 | "K-Neighbors, or KNN, uses proximity to make classifications or predictions based on how its neighbours are classified, working off the assumption that similar points can be found near one another." |
||
| 3456 | ], |
||
| 3457 | "metadata": { |
||
| 3458 | "id": "iDBtAXCLFk-5" |
||
| 3459 | } |
||
| 3460 | }, |
||
| 3461 | { |
||
| 3462 | "cell_type": "code", |
||
| 3463 | "source": [ |
||
| 3464 | "def k_neighbors_model(df):\n", |
||
| 3465 | " df_test = df.copy()\n", |
||
| 3466 | " target = 'ICU'\n", |
||
| 3467 | " y = df_test.pop(target)\n", |
||
| 3468 | " X = df_test\n", |
||
| 3469 | " X_train, X_test, y_train, y_test = train_test_split(X_data, Y_data, test_size = 0.30, stratify = Y_data)\n", |
||
| 3470 | "\n", |
||
| 3471 | " print(f'Training data: {X_train.shape}')\n", |
||
| 3472 | " print(f'Test data: {X_test.shape}\\n')\n", |
||
| 3473 | "\n", |
||
| 3474 | " KNN_model = KNeighborsClassifier(n_neighbors=25,p=1)\n", |
||
| 3475 | " KNN_model.fit(X_train,y_train)\n", |
||
| 3476 | " y_test_pred = KNN_model.predict(X_test)\n", |
||
| 3477 | "\n", |
||
| 3478 | " print(metrics.classification_report(y_test, y_test_pred))\n", |
||
| 3479 | "\n", |
||
| 3480 | " print(\"ROC_AUC_Score:\",roc_auc_score(y_test, y_test_pred))\n", |
||
| 3481 | "\n", |
||
| 3482 | " plt.figure(figsize = (7, 5))\n", |
||
| 3483 | " sns.heatmap(confusion_matrix(y_test, y_test_pred), annot = True, cmap = 'CMRmap_r', fmt = \"d\", cbar = True, xticklabels = ['No', 'Yes'], yticklabels = ['No', 'Yes'], annot_kws = {\"fontsize\":15})\n", |
||
| 3484 | " plt.show()\n", |
||
| 3485 | "\n", |
||
| 3486 | " return" |
||
| 3487 | ], |
||
| 3488 | "metadata": { |
||
| 3489 | "id": "RP9CuB1MAJ9V" |
||
| 3490 | }, |
||
| 3491 | "execution_count": 29, |
||
| 3492 | "outputs": [] |
||
| 3493 | }, |
||
| 3494 | { |
||
| 3495 | "cell_type": "code", |
||
| 3496 | "source": [ |
||
| 3497 | "# score_all = k_neighbors_model(filled_data)" |
||
| 3498 | ], |
||
| 3499 | "metadata": { |
||
| 3500 | "id": "Mhge3eFCAflN" |
||
| 3501 | }, |
||
| 3502 | "execution_count": null, |
||
| 3503 | "outputs": [] |
||
| 3504 | }, |
||
| 3505 | { |
||
| 3506 | "cell_type": "markdown", |
||
| 3507 | "source": [ |
||
| 3508 | "# Data Cleaning & Model Testing\n", |
||
| 3509 | "Prepare the data for the models and test the models prediction abilities." |
||
| 3510 | ], |
||
| 3511 | "metadata": { |
||
| 3512 | "id": "1mvFtOkcxmiH" |
||
| 3513 | } |
||
| 3514 | }, |
||
| 3515 | { |
||
| 3516 | "cell_type": "markdown", |
||
| 3517 | "source": [ |
||
| 3518 | "Once a patient is admitted to ICU, their data cannot be used for modeling as we need to predict the need for future ICU admission rather than at the present moment. So we give the machine learning algorithm data from the windows before ICU admission. \n", |
||
| 3519 | "\n", |
||
| 3520 | "For the model to make more clinically relevant predictions, it will receive data only from the first window time frame of 0-2 hours and then from all windows except the last, above 12 hours. The earlier the model can predict the need for ICU treatment of the patient, the more it becomes clinically relevant." |
||
| 3521 | ], |
||
| 3522 | "metadata": { |
||
| 3523 | "id": "N0mx3C-bB_ld" |
||
| 3524 | } |
||
| 3525 | }, |
||
| 3526 | { |
||
| 3527 | "cell_type": "code", |
||
| 3528 | "source": [ |
||
| 3529 | "# # Remove patients admitted to ICU in windows 2-4, 4-6, 6-12, and above 12\n", |
||
| 3530 | "model_data = filled_data.copy()\n", |
||
| 3531 | "\n", |
||
| 3532 | "window_0_2 = model_data[(model_data['WINDOW'] == 1)|(model_data['WINDOW'] == 2)|(model_data['WINDOW'] == 3)|(model_data['WINDOW'] == 4) & (model_data['ICU'] == 1)].index\n", |
||
| 3533 | "model_data.drop(window_0_2, inplace=True)\n", |
||
| 3534 | "\n", |
||
| 3535 | "model_data.head(15)" |
||
| 3536 | ], |
||
| 3537 | "metadata": { |
||
| 3538 | "id": "SGqblEB9COQE", |
||
| 3539 | "colab": { |
||
| 3540 | "base_uri": "https://localhost:8080/", |
||
| 3541 | "height": 648 |
||
| 3542 | }, |
||
| 3543 | "outputId": "f0a7ff63-24f1-4dc1-cd20-1759ddc483a3" |
||
| 3544 | }, |
||
| 3545 | "execution_count": 30, |
||
| 3546 | "outputs": [ |
||
| 3547 | { |
||
| 3548 | "output_type": "execute_result", |
||
| 3549 | "data": { |
||
| 3550 | "text/plain": [ |
||
| 3551 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 3552 | "0 0 1 5 0 \n", |
||
| 3553 | "5 1 1 8 1 \n", |
||
| 3554 | "10 2 0 0 0 \n", |
||
| 3555 | "15 3 0 3 1 \n", |
||
| 3556 | "19 3 0 3 1 \n", |
||
| 3557 | "20 4 0 0 0 \n", |
||
| 3558 | "24 4 0 0 0 \n", |
||
| 3559 | "25 5 0 0 0 \n", |
||
| 3560 | "29 5 0 0 0 \n", |
||
| 3561 | "30 6 1 6 1 \n", |
||
| 3562 | "34 6 1 6 1 \n", |
||
| 3563 | "35 7 0 1 0 \n", |
||
| 3564 | "39 7 0 1 0 \n", |
||
| 3565 | "40 8 0 4 0 \n", |
||
| 3566 | "44 8 0 4 0 \n", |
||
| 3567 | "\n", |
||
| 3568 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 3569 | "0 0.0 0.0 0.0 \n", |
||
| 3570 | "5 0.0 0.0 0.0 \n", |
||
| 3571 | "10 0.0 0.0 0.0 \n", |
||
| 3572 | "15 0.0 0.0 0.0 \n", |
||
| 3573 | "19 0.0 0.0 0.0 \n", |
||
| 3574 | "20 0.0 0.0 0.0 \n", |
||
| 3575 | "24 0.0 0.0 0.0 \n", |
||
| 3576 | "25 0.0 0.0 0.0 \n", |
||
| 3577 | "29 0.0 0.0 0.0 \n", |
||
| 3578 | "30 0.0 0.0 0.0 \n", |
||
| 3579 | "34 0.0 0.0 0.0 \n", |
||
| 3580 | "35 0.0 0.0 0.0 \n", |
||
| 3581 | "39 0.0 0.0 0.0 \n", |
||
| 3582 | "40 0.0 0.0 0.0 \n", |
||
| 3583 | "44 1.0 0.0 0.0 \n", |
||
| 3584 | "\n", |
||
| 3585 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 3586 | "0 0.0 1.0 1.0 ... \n", |
||
| 3587 | "5 0.0 0.0 0.0 ... \n", |
||
| 3588 | "10 0.0 0.0 0.0 ... \n", |
||
| 3589 | "15 0.0 0.0 0.0 ... \n", |
||
| 3590 | "19 0.0 0.0 0.0 ... \n", |
||
| 3591 | "20 0.0 0.0 0.0 ... \n", |
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| 3592 | "24 0.0 0.0 0.0 ... \n", |
||
| 3593 | "25 0.0 0.0 0.0 ... \n", |
||
| 3594 | "29 0.0 0.0 0.0 ... \n", |
||
| 3595 | "30 0.0 0.0 0.0 ... \n", |
||
| 3596 | "34 0.0 0.0 0.0 ... \n", |
||
| 3597 | "35 0.0 0.0 0.0 ... \n", |
||
| 3598 | "39 0.0 0.0 0.0 ... \n", |
||
| 3599 | "40 0.0 0.0 0.0 ... \n", |
||
| 3600 | "44 0.0 0.0 0.0 ... \n", |
||
| 3601 | "\n", |
||
| 3602 | " OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n", |
||
| 3603 | "0 -1.000000 -1.000000 \n", |
||
| 3604 | "5 -1.000000 -1.000000 \n", |
||
| 3605 | "10 -0.959596 -0.515528 \n", |
||
| 3606 | "15 -1.000000 -1.000000 \n", |
||
| 3607 | "19 -0.171717 -0.308696 \n", |
||
| 3608 | "20 -0.979798 -1.000000 \n", |
||
| 3609 | "24 -0.939394 -0.652174 \n", |
||
| 3610 | "25 -0.979798 -0.860870 \n", |
||
| 3611 | "29 -0.919192 -0.758651 \n", |
||
| 3612 | "30 -1.000000 -1.000000 \n", |
||
| 3613 | "34 0.858586 -0.884058 \n", |
||
| 3614 | "35 -1.000000 -1.000000 \n", |
||
| 3615 | "39 -0.919192 -0.689777 \n", |
||
| 3616 | "40 -1.000000 -1.000000 \n", |
||
| 3617 | "44 -0.898990 -0.701863 \n", |
||
| 3618 | "\n", |
||
| 3619 | " BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n", |
||
| 3620 | "0 -1.000000 -1.000000 \n", |
||
| 3621 | "5 -1.000000 -1.000000 \n", |
||
| 3622 | "10 -0.351328 -0.747001 \n", |
||
| 3623 | "15 -1.000000 -1.000000 \n", |
||
| 3624 | "19 -0.057718 -0.069094 \n", |
||
| 3625 | "20 -0.883669 -0.956805 \n", |
||
| 3626 | "24 -0.596165 -0.634847 \n", |
||
| 3627 | "25 -0.714460 -0.986481 \n", |
||
| 3628 | "29 -0.683267 -0.581849 \n", |
||
| 3629 | "30 -1.000000 -1.000000 \n", |
||
| 3630 | "34 -0.637584 -0.781230 \n", |
||
| 3631 | "35 -1.000000 -1.000000 \n", |
||
| 3632 | "39 -0.555034 -0.449556 \n", |
||
| 3633 | "40 -1.000000 -1.000000 \n", |
||
| 3634 | "44 -0.571690 -0.719780 \n", |
||
| 3635 | "\n", |
||
| 3636 | " RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 3637 | "0 -1.000000 -1.000000 \n", |
||
| 3638 | "5 -1.000000 -1.000000 \n", |
||
| 3639 | "10 -0.756272 -1.000000 \n", |
||
| 3640 | "15 -1.000000 -1.000000 \n", |
||
| 3641 | "19 -0.329749 -0.047619 \n", |
||
| 3642 | "20 -0.870968 -0.953536 \n", |
||
| 3643 | "24 -0.817204 -0.645793 \n", |
||
| 3644 | "25 -1.000000 -0.975891 \n", |
||
| 3645 | "29 -0.939068 -0.736640 \n", |
||
| 3646 | "30 -1.000000 -1.000000 \n", |
||
| 3647 | "34 -0.826825 -0.672101 \n", |
||
| 3648 | "35 -1.000000 -1.000000 \n", |
||
| 3649 | "39 -0.817204 -0.690476 \n", |
||
| 3650 | "40 -1.000000 -1.000000 \n", |
||
| 3651 | "44 -0.741935 -0.240194 \n", |
||
| 3652 | "\n", |
||
| 3653 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU ICU_SUM \n", |
||
| 3654 | "0 -1.000000 0 0 1 \n", |
||
| 3655 | "5 -1.000000 0 1 1 \n", |
||
| 3656 | "10 -0.961262 0 0 1 \n", |
||
| 3657 | "15 -1.000000 0 0 0 \n", |
||
| 3658 | "19 -0.172436 4 0 0 \n", |
||
| 3659 | "20 -0.980333 0 0 0 \n", |
||
| 3660 | "24 -0.940077 4 0 0 \n", |
||
| 3661 | "25 -0.980129 0 0 0 \n", |
||
| 3662 | "29 -0.920927 4 0 0 \n", |
||
| 3663 | "30 -1.000000 0 0 0 \n", |
||
| 3664 | "34 0.917526 4 0 0 \n", |
||
| 3665 | "35 -1.000000 0 0 0 \n", |
||
| 3666 | "39 -0.920103 4 0 0 \n", |
||
| 3667 | "40 -1.000000 0 0 0 \n", |
||
| 3668 | "44 -0.898004 4 0 0 \n", |
||
| 3669 | "\n", |
||
| 3670 | "[15 rows x 232 columns]" |
||
| 3671 | ], |
||
| 3672 | "text/html": [ |
||
| 3673 | "\n", |
||
| 3674 | " <div id=\"df-0cb73786-7711-47b8-ac65-cfebdd456d21\">\n", |
||
| 3675 | " <div class=\"colab-df-container\">\n", |
||
| 3676 | " <div>\n", |
||
| 3677 | "<style scoped>\n", |
||
| 3678 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 3679 | " vertical-align: middle;\n", |
||
| 3680 | " }\n", |
||
| 3681 | "\n", |
||
| 3682 | " .dataframe tbody tr th {\n", |
||
| 3683 | " vertical-align: top;\n", |
||
| 3684 | " }\n", |
||
| 3685 | "\n", |
||
| 3686 | " .dataframe thead th {\n", |
||
| 3687 | " text-align: right;\n", |
||
| 3688 | " }\n", |
||
| 3689 | "</style>\n", |
||
| 3690 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 3691 | " <thead>\n", |
||
| 3692 | " <tr style=\"text-align: right;\">\n", |
||
| 3693 | " <th></th>\n", |
||
| 3694 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 3695 | " <th>AGE_ABOVE65</th>\n", |
||
| 3696 | " <th>AGE_PERCENTIL</th>\n", |
||
| 3697 | " <th>GENDER</th>\n", |
||
| 3698 | " <th>DISEASE GROUPING 1</th>\n", |
||
| 3699 | " <th>DISEASE GROUPING 2</th>\n", |
||
| 3700 | " <th>DISEASE GROUPING 3</th>\n", |
||
| 3701 | " <th>DISEASE GROUPING 4</th>\n", |
||
| 3702 | " <th>DISEASE GROUPING 5</th>\n", |
||
| 3703 | " <th>DISEASE GROUPING 6</th>\n", |
||
| 3704 | " <th>...</th>\n", |
||
| 3705 | " <th>OXYGEN_SATURATION_DIFF</th>\n", |
||
| 3706 | " <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n", |
||
| 3707 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
||
| 3708 | " <th>HEART_RATE_DIFF_REL</th>\n", |
||
| 3709 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
||
| 3710 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
||
| 3711 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
||
| 3712 | " <th>WINDOW</th>\n", |
||
| 3713 | " <th>ICU</th>\n", |
||
| 3714 | " <th>ICU_SUM</th>\n", |
||
| 3715 | " </tr>\n", |
||
| 3716 | " </thead>\n", |
||
| 3717 | " <tbody>\n", |
||
| 3718 | " <tr>\n", |
||
| 3719 | " <th>0</th>\n", |
||
| 3720 | " <td>0</td>\n", |
||
| 3721 | " <td>1</td>\n", |
||
| 3722 | " <td>5</td>\n", |
||
| 3723 | " <td>0</td>\n", |
||
| 3724 | " <td>0.0</td>\n", |
||
| 3725 | " <td>0.0</td>\n", |
||
| 3726 | " <td>0.0</td>\n", |
||
| 3727 | " <td>0.0</td>\n", |
||
| 3728 | " <td>1.0</td>\n", |
||
| 3729 | " <td>1.0</td>\n", |
||
| 3730 | " <td>...</td>\n", |
||
| 3731 | " <td>-1.000000</td>\n", |
||
| 3732 | " <td>-1.000000</td>\n", |
||
| 3733 | " <td>-1.000000</td>\n", |
||
| 3734 | " <td>-1.000000</td>\n", |
||
| 3735 | " <td>-1.000000</td>\n", |
||
| 3736 | " <td>-1.000000</td>\n", |
||
| 3737 | " <td>-1.000000</td>\n", |
||
| 3738 | " <td>0</td>\n", |
||
| 3739 | " <td>0</td>\n", |
||
| 3740 | " <td>1</td>\n", |
||
| 3741 | " </tr>\n", |
||
| 3742 | " <tr>\n", |
||
| 3743 | " <th>5</th>\n", |
||
| 3744 | " <td>1</td>\n", |
||
| 3745 | " <td>1</td>\n", |
||
| 3746 | " <td>8</td>\n", |
||
| 3747 | " <td>1</td>\n", |
||
| 3748 | " <td>0.0</td>\n", |
||
| 3749 | " <td>0.0</td>\n", |
||
| 3750 | " <td>0.0</td>\n", |
||
| 3751 | " <td>0.0</td>\n", |
||
| 3752 | " <td>0.0</td>\n", |
||
| 3753 | " <td>0.0</td>\n", |
||
| 3754 | " <td>...</td>\n", |
||
| 3755 | " <td>-1.000000</td>\n", |
||
| 3756 | " <td>-1.000000</td>\n", |
||
| 3757 | " <td>-1.000000</td>\n", |
||
| 3758 | " <td>-1.000000</td>\n", |
||
| 3759 | " <td>-1.000000</td>\n", |
||
| 3760 | " <td>-1.000000</td>\n", |
||
| 3761 | " <td>-1.000000</td>\n", |
||
| 3762 | " <td>0</td>\n", |
||
| 3763 | " <td>1</td>\n", |
||
| 3764 | " <td>1</td>\n", |
||
| 3765 | " </tr>\n", |
||
| 3766 | " <tr>\n", |
||
| 3767 | " <th>10</th>\n", |
||
| 3768 | " <td>2</td>\n", |
||
| 3769 | " <td>0</td>\n", |
||
| 3770 | " <td>0</td>\n", |
||
| 3771 | " <td>0</td>\n", |
||
| 3772 | " <td>0.0</td>\n", |
||
| 3773 | " <td>0.0</td>\n", |
||
| 3774 | " <td>0.0</td>\n", |
||
| 3775 | " <td>0.0</td>\n", |
||
| 3776 | " <td>0.0</td>\n", |
||
| 3777 | " <td>0.0</td>\n", |
||
| 3778 | " <td>...</td>\n", |
||
| 3779 | " <td>-0.959596</td>\n", |
||
| 3780 | " <td>-0.515528</td>\n", |
||
| 3781 | " <td>-0.351328</td>\n", |
||
| 3782 | " <td>-0.747001</td>\n", |
||
| 3783 | " <td>-0.756272</td>\n", |
||
| 3784 | " <td>-1.000000</td>\n", |
||
| 3785 | " <td>-0.961262</td>\n", |
||
| 3786 | " <td>0</td>\n", |
||
| 3787 | " <td>0</td>\n", |
||
| 3788 | " <td>1</td>\n", |
||
| 3789 | " </tr>\n", |
||
| 3790 | " <tr>\n", |
||
| 3791 | " <th>15</th>\n", |
||
| 3792 | " <td>3</td>\n", |
||
| 3793 | " <td>0</td>\n", |
||
| 3794 | " <td>3</td>\n", |
||
| 3795 | " <td>1</td>\n", |
||
| 3796 | " <td>0.0</td>\n", |
||
| 3797 | " <td>0.0</td>\n", |
||
| 3798 | " <td>0.0</td>\n", |
||
| 3799 | " <td>0.0</td>\n", |
||
| 3800 | " <td>0.0</td>\n", |
||
| 3801 | " <td>0.0</td>\n", |
||
| 3802 | " <td>...</td>\n", |
||
| 3803 | " <td>-1.000000</td>\n", |
||
| 3804 | " <td>-1.000000</td>\n", |
||
| 3805 | " <td>-1.000000</td>\n", |
||
| 3806 | " <td>-1.000000</td>\n", |
||
| 3807 | " <td>-1.000000</td>\n", |
||
| 3808 | " <td>-1.000000</td>\n", |
||
| 3809 | " <td>-1.000000</td>\n", |
||
| 3810 | " <td>0</td>\n", |
||
| 3811 | " <td>0</td>\n", |
||
| 3812 | " <td>0</td>\n", |
||
| 3813 | " </tr>\n", |
||
| 3814 | " <tr>\n", |
||
| 3815 | " <th>19</th>\n", |
||
| 3816 | " <td>3</td>\n", |
||
| 3817 | " <td>0</td>\n", |
||
| 3818 | " <td>3</td>\n", |
||
| 3819 | " <td>1</td>\n", |
||
| 3820 | " <td>0.0</td>\n", |
||
| 3821 | " <td>0.0</td>\n", |
||
| 3822 | " <td>0.0</td>\n", |
||
| 3823 | " <td>0.0</td>\n", |
||
| 3824 | " <td>0.0</td>\n", |
||
| 3825 | " <td>0.0</td>\n", |
||
| 3826 | " <td>...</td>\n", |
||
| 3827 | " <td>-0.171717</td>\n", |
||
| 3828 | " <td>-0.308696</td>\n", |
||
| 3829 | " <td>-0.057718</td>\n", |
||
| 3830 | " <td>-0.069094</td>\n", |
||
| 3831 | " <td>-0.329749</td>\n", |
||
| 3832 | " <td>-0.047619</td>\n", |
||
| 3833 | " <td>-0.172436</td>\n", |
||
| 3834 | " <td>4</td>\n", |
||
| 3835 | " <td>0</td>\n", |
||
| 3836 | " <td>0</td>\n", |
||
| 3837 | " </tr>\n", |
||
| 3838 | " <tr>\n", |
||
| 3839 | " <th>20</th>\n", |
||
| 3840 | " <td>4</td>\n", |
||
| 3841 | " <td>0</td>\n", |
||
| 3842 | " <td>0</td>\n", |
||
| 3843 | " <td>0</td>\n", |
||
| 3844 | " <td>0.0</td>\n", |
||
| 3845 | " <td>0.0</td>\n", |
||
| 3846 | " <td>0.0</td>\n", |
||
| 3847 | " <td>0.0</td>\n", |
||
| 3848 | " <td>0.0</td>\n", |
||
| 3849 | " <td>0.0</td>\n", |
||
| 3850 | " <td>...</td>\n", |
||
| 3851 | " <td>-0.979798</td>\n", |
||
| 3852 | " <td>-1.000000</td>\n", |
||
| 3853 | " <td>-0.883669</td>\n", |
||
| 3854 | " <td>-0.956805</td>\n", |
||
| 3855 | " <td>-0.870968</td>\n", |
||
| 3856 | " <td>-0.953536</td>\n", |
||
| 3857 | " <td>-0.980333</td>\n", |
||
| 3858 | " <td>0</td>\n", |
||
| 3859 | " <td>0</td>\n", |
||
| 3860 | " <td>0</td>\n", |
||
| 3861 | " </tr>\n", |
||
| 3862 | " <tr>\n", |
||
| 3863 | " <th>24</th>\n", |
||
| 3864 | " <td>4</td>\n", |
||
| 3865 | " <td>0</td>\n", |
||
| 3866 | " <td>0</td>\n", |
||
| 3867 | " <td>0</td>\n", |
||
| 3868 | " <td>0.0</td>\n", |
||
| 3869 | " <td>0.0</td>\n", |
||
| 3870 | " <td>0.0</td>\n", |
||
| 3871 | " <td>0.0</td>\n", |
||
| 3872 | " <td>0.0</td>\n", |
||
| 3873 | " <td>0.0</td>\n", |
||
| 3874 | " <td>...</td>\n", |
||
| 3875 | " <td>-0.939394</td>\n", |
||
| 3876 | " <td>-0.652174</td>\n", |
||
| 3877 | " <td>-0.596165</td>\n", |
||
| 3878 | " <td>-0.634847</td>\n", |
||
| 3879 | " <td>-0.817204</td>\n", |
||
| 3880 | " <td>-0.645793</td>\n", |
||
| 3881 | " <td>-0.940077</td>\n", |
||
| 3882 | " <td>4</td>\n", |
||
| 3883 | " <td>0</td>\n", |
||
| 3884 | " <td>0</td>\n", |
||
| 3885 | " </tr>\n", |
||
| 3886 | " <tr>\n", |
||
| 3887 | " <th>25</th>\n", |
||
| 3888 | " <td>5</td>\n", |
||
| 3889 | " <td>0</td>\n", |
||
| 3890 | " <td>0</td>\n", |
||
| 3891 | " <td>0</td>\n", |
||
| 3892 | " <td>0.0</td>\n", |
||
| 3893 | " <td>0.0</td>\n", |
||
| 3894 | " <td>0.0</td>\n", |
||
| 3895 | " <td>0.0</td>\n", |
||
| 3896 | " <td>0.0</td>\n", |
||
| 3897 | " <td>0.0</td>\n", |
||
| 3898 | " <td>...</td>\n", |
||
| 3899 | " <td>-0.979798</td>\n", |
||
| 3900 | " <td>-0.860870</td>\n", |
||
| 3901 | " <td>-0.714460</td>\n", |
||
| 3902 | " <td>-0.986481</td>\n", |
||
| 3903 | " <td>-1.000000</td>\n", |
||
| 3904 | " <td>-0.975891</td>\n", |
||
| 3905 | " <td>-0.980129</td>\n", |
||
| 3906 | " <td>0</td>\n", |
||
| 3907 | " <td>0</td>\n", |
||
| 3908 | " <td>0</td>\n", |
||
| 3909 | " </tr>\n", |
||
| 3910 | " <tr>\n", |
||
| 3911 | " <th>29</th>\n", |
||
| 3912 | " <td>5</td>\n", |
||
| 3913 | " <td>0</td>\n", |
||
| 3914 | " <td>0</td>\n", |
||
| 3915 | " <td>0</td>\n", |
||
| 3916 | " <td>0.0</td>\n", |
||
| 3917 | " <td>0.0</td>\n", |
||
| 3918 | " <td>0.0</td>\n", |
||
| 3919 | " <td>0.0</td>\n", |
||
| 3920 | " <td>0.0</td>\n", |
||
| 3921 | " <td>0.0</td>\n", |
||
| 3922 | " <td>...</td>\n", |
||
| 3923 | " <td>-0.919192</td>\n", |
||
| 3924 | " <td>-0.758651</td>\n", |
||
| 3925 | " <td>-0.683267</td>\n", |
||
| 3926 | " <td>-0.581849</td>\n", |
||
| 3927 | " <td>-0.939068</td>\n", |
||
| 3928 | " <td>-0.736640</td>\n", |
||
| 3929 | " <td>-0.920927</td>\n", |
||
| 3930 | " <td>4</td>\n", |
||
| 3931 | " <td>0</td>\n", |
||
| 3932 | " <td>0</td>\n", |
||
| 3933 | " </tr>\n", |
||
| 3934 | " <tr>\n", |
||
| 3935 | " <th>30</th>\n", |
||
| 3936 | " <td>6</td>\n", |
||
| 3937 | " <td>1</td>\n", |
||
| 3938 | " <td>6</td>\n", |
||
| 3939 | " <td>1</td>\n", |
||
| 3940 | " <td>0.0</td>\n", |
||
| 3941 | " <td>0.0</td>\n", |
||
| 3942 | " <td>0.0</td>\n", |
||
| 3943 | " <td>0.0</td>\n", |
||
| 3944 | " <td>0.0</td>\n", |
||
| 3945 | " <td>0.0</td>\n", |
||
| 3946 | " <td>...</td>\n", |
||
| 3947 | " <td>-1.000000</td>\n", |
||
| 3948 | " <td>-1.000000</td>\n", |
||
| 3949 | " <td>-1.000000</td>\n", |
||
| 3950 | " <td>-1.000000</td>\n", |
||
| 3951 | " <td>-1.000000</td>\n", |
||
| 3952 | " <td>-1.000000</td>\n", |
||
| 3953 | " <td>-1.000000</td>\n", |
||
| 3954 | " <td>0</td>\n", |
||
| 3955 | " <td>0</td>\n", |
||
| 3956 | " <td>0</td>\n", |
||
| 3957 | " </tr>\n", |
||
| 3958 | " <tr>\n", |
||
| 3959 | " <th>34</th>\n", |
||
| 3960 | " <td>6</td>\n", |
||
| 3961 | " <td>1</td>\n", |
||
| 3962 | " <td>6</td>\n", |
||
| 3963 | " <td>1</td>\n", |
||
| 3964 | " <td>0.0</td>\n", |
||
| 3965 | " <td>0.0</td>\n", |
||
| 3966 | " <td>0.0</td>\n", |
||
| 3967 | " <td>0.0</td>\n", |
||
| 3968 | " <td>0.0</td>\n", |
||
| 3969 | " <td>0.0</td>\n", |
||
| 3970 | " <td>...</td>\n", |
||
| 3971 | " <td>0.858586</td>\n", |
||
| 3972 | " <td>-0.884058</td>\n", |
||
| 3973 | " <td>-0.637584</td>\n", |
||
| 3974 | " <td>-0.781230</td>\n", |
||
| 3975 | " <td>-0.826825</td>\n", |
||
| 3976 | " <td>-0.672101</td>\n", |
||
| 3977 | " <td>0.917526</td>\n", |
||
| 3978 | " <td>4</td>\n", |
||
| 3979 | " <td>0</td>\n", |
||
| 3980 | " <td>0</td>\n", |
||
| 3981 | " </tr>\n", |
||
| 3982 | " <tr>\n", |
||
| 3983 | " <th>35</th>\n", |
||
| 3984 | " <td>7</td>\n", |
||
| 3985 | " <td>0</td>\n", |
||
| 3986 | " <td>1</td>\n", |
||
| 3987 | " <td>0</td>\n", |
||
| 3988 | " <td>0.0</td>\n", |
||
| 3989 | " <td>0.0</td>\n", |
||
| 3990 | " <td>0.0</td>\n", |
||
| 3991 | " <td>0.0</td>\n", |
||
| 3992 | " <td>0.0</td>\n", |
||
| 3993 | " <td>0.0</td>\n", |
||
| 3994 | " <td>...</td>\n", |
||
| 3995 | " <td>-1.000000</td>\n", |
||
| 3996 | " <td>-1.000000</td>\n", |
||
| 3997 | " <td>-1.000000</td>\n", |
||
| 3998 | " <td>-1.000000</td>\n", |
||
| 3999 | " <td>-1.000000</td>\n", |
||
| 4000 | " <td>-1.000000</td>\n", |
||
| 4001 | " <td>-1.000000</td>\n", |
||
| 4002 | " <td>0</td>\n", |
||
| 4003 | " <td>0</td>\n", |
||
| 4004 | " <td>0</td>\n", |
||
| 4005 | " </tr>\n", |
||
| 4006 | " <tr>\n", |
||
| 4007 | " <th>39</th>\n", |
||
| 4008 | " <td>7</td>\n", |
||
| 4009 | " <td>0</td>\n", |
||
| 4010 | " <td>1</td>\n", |
||
| 4011 | " <td>0</td>\n", |
||
| 4012 | " <td>0.0</td>\n", |
||
| 4013 | " <td>0.0</td>\n", |
||
| 4014 | " <td>0.0</td>\n", |
||
| 4015 | " <td>0.0</td>\n", |
||
| 4016 | " <td>0.0</td>\n", |
||
| 4017 | " <td>0.0</td>\n", |
||
| 4018 | " <td>...</td>\n", |
||
| 4019 | " <td>-0.919192</td>\n", |
||
| 4020 | " <td>-0.689777</td>\n", |
||
| 4021 | " <td>-0.555034</td>\n", |
||
| 4022 | " <td>-0.449556</td>\n", |
||
| 4023 | " <td>-0.817204</td>\n", |
||
| 4024 | " <td>-0.690476</td>\n", |
||
| 4025 | " <td>-0.920103</td>\n", |
||
| 4026 | " <td>4</td>\n", |
||
| 4027 | " <td>0</td>\n", |
||
| 4028 | " <td>0</td>\n", |
||
| 4029 | " </tr>\n", |
||
| 4030 | " <tr>\n", |
||
| 4031 | " <th>40</th>\n", |
||
| 4032 | " <td>8</td>\n", |
||
| 4033 | " <td>0</td>\n", |
||
| 4034 | " <td>4</td>\n", |
||
| 4035 | " <td>0</td>\n", |
||
| 4036 | " <td>0.0</td>\n", |
||
| 4037 | " <td>0.0</td>\n", |
||
| 4038 | " <td>0.0</td>\n", |
||
| 4039 | " <td>0.0</td>\n", |
||
| 4040 | " <td>0.0</td>\n", |
||
| 4041 | " <td>0.0</td>\n", |
||
| 4042 | " <td>...</td>\n", |
||
| 4043 | " <td>-1.000000</td>\n", |
||
| 4044 | " <td>-1.000000</td>\n", |
||
| 4045 | " <td>-1.000000</td>\n", |
||
| 4046 | " <td>-1.000000</td>\n", |
||
| 4047 | " <td>-1.000000</td>\n", |
||
| 4048 | " <td>-1.000000</td>\n", |
||
| 4049 | " <td>-1.000000</td>\n", |
||
| 4050 | " <td>0</td>\n", |
||
| 4051 | " <td>0</td>\n", |
||
| 4052 | " <td>0</td>\n", |
||
| 4053 | " </tr>\n", |
||
| 4054 | " <tr>\n", |
||
| 4055 | " <th>44</th>\n", |
||
| 4056 | " <td>8</td>\n", |
||
| 4057 | " <td>0</td>\n", |
||
| 4058 | " <td>4</td>\n", |
||
| 4059 | " <td>0</td>\n", |
||
| 4060 | " <td>1.0</td>\n", |
||
| 4061 | " <td>0.0</td>\n", |
||
| 4062 | " <td>0.0</td>\n", |
||
| 4063 | " <td>0.0</td>\n", |
||
| 4064 | " <td>0.0</td>\n", |
||
| 4065 | " <td>0.0</td>\n", |
||
| 4066 | " <td>...</td>\n", |
||
| 4067 | " <td>-0.898990</td>\n", |
||
| 4068 | " <td>-0.701863</td>\n", |
||
| 4069 | " <td>-0.571690</td>\n", |
||
| 4070 | " <td>-0.719780</td>\n", |
||
| 4071 | " <td>-0.741935</td>\n", |
||
| 4072 | " <td>-0.240194</td>\n", |
||
| 4073 | " <td>-0.898004</td>\n", |
||
| 4074 | " <td>4</td>\n", |
||
| 4075 | " <td>0</td>\n", |
||
| 4076 | " <td>0</td>\n", |
||
| 4077 | " </tr>\n", |
||
| 4078 | " </tbody>\n", |
||
| 4079 | "</table>\n", |
||
| 4080 | "<p>15 rows × 232 columns</p>\n", |
||
| 4081 | "</div>\n", |
||
| 4082 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cb73786-7711-47b8-ac65-cfebdd456d21')\"\n", |
||
| 4083 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 4084 | " style=\"display:none;\">\n", |
||
| 4085 | " \n", |
||
| 4086 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 4087 | " width=\"24px\">\n", |
||
| 4088 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 4089 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 4090 | " </svg>\n", |
||
| 4091 | " </button>\n", |
||
| 4092 | " \n", |
||
| 4093 | " <style>\n", |
||
| 4094 | " .colab-df-container {\n", |
||
| 4095 | " display:flex;\n", |
||
| 4096 | " flex-wrap:wrap;\n", |
||
| 4097 | " gap: 12px;\n", |
||
| 4098 | " }\n", |
||
| 4099 | "\n", |
||
| 4100 | " .colab-df-convert {\n", |
||
| 4101 | " background-color: #E8F0FE;\n", |
||
| 4102 | " border: none;\n", |
||
| 4103 | " border-radius: 50%;\n", |
||
| 4104 | " cursor: pointer;\n", |
||
| 4105 | " display: none;\n", |
||
| 4106 | " fill: #1967D2;\n", |
||
| 4107 | " height: 32px;\n", |
||
| 4108 | " padding: 0 0 0 0;\n", |
||
| 4109 | " width: 32px;\n", |
||
| 4110 | " }\n", |
||
| 4111 | "\n", |
||
| 4112 | " .colab-df-convert:hover {\n", |
||
| 4113 | " background-color: #E2EBFA;\n", |
||
| 4114 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 4115 | " fill: #174EA6;\n", |
||
| 4116 | " }\n", |
||
| 4117 | "\n", |
||
| 4118 | " [theme=dark] .colab-df-convert {\n", |
||
| 4119 | " background-color: #3B4455;\n", |
||
| 4120 | " fill: #D2E3FC;\n", |
||
| 4121 | " }\n", |
||
| 4122 | "\n", |
||
| 4123 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 4124 | " background-color: #434B5C;\n", |
||
| 4125 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 4126 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 4127 | " fill: #FFFFFF;\n", |
||
| 4128 | " }\n", |
||
| 4129 | " </style>\n", |
||
| 4130 | "\n", |
||
| 4131 | " <script>\n", |
||
| 4132 | " const buttonEl =\n", |
||
| 4133 | " document.querySelector('#df-0cb73786-7711-47b8-ac65-cfebdd456d21 button.colab-df-convert');\n", |
||
| 4134 | " buttonEl.style.display =\n", |
||
| 4135 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 4136 | "\n", |
||
| 4137 | " async function convertToInteractive(key) {\n", |
||
| 4138 | " const element = document.querySelector('#df-0cb73786-7711-47b8-ac65-cfebdd456d21');\n", |
||
| 4139 | " const dataTable =\n", |
||
| 4140 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 4141 | " [key], {});\n", |
||
| 4142 | " if (!dataTable) return;\n", |
||
| 4143 | "\n", |
||
| 4144 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 4145 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 4146 | " + ' to learn more about interactive tables.';\n", |
||
| 4147 | " element.innerHTML = '';\n", |
||
| 4148 | " dataTable['output_type'] = 'display_data';\n", |
||
| 4149 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 4150 | " const docLink = document.createElement('div');\n", |
||
| 4151 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 4152 | " element.appendChild(docLink);\n", |
||
| 4153 | " }\n", |
||
| 4154 | " </script>\n", |
||
| 4155 | " </div>\n", |
||
| 4156 | " </div>\n", |
||
| 4157 | " " |
||
| 4158 | ] |
||
| 4159 | }, |
||
| 4160 | "metadata": {}, |
||
| 4161 | "execution_count": 30 |
||
| 4162 | } |
||
| 4163 | ] |
||
| 4164 | }, |
||
| 4165 | { |
||
| 4166 | "cell_type": "markdown", |
||
| 4167 | "source": [ |
||
| 4168 | "*Precision*: the percent of correct predictions to the total number of predictions made by the model\n", |
||
| 4169 | "\n", |
||
| 4170 | "*Recall*: the percent of correct predictions to the actual number of positive, ICU = 1 (true positive), or negative, ICU = 0 (true negative), instances\n", |
||
| 4171 | "\n", |
||
| 4172 | "*F1-score*: the harmonic mean of precision and recall; weighted average of F1 is used to compare model efficiency over the accuracy score; the closer to 1, the better the model is at making correct predictions\n", |
||
| 4173 | "\n", |
||
| 4174 | "*Roc-Auc score*: indicates model efficiency\n", |
||
| 4175 | "\n", |
||
| 4176 | "*Confusion Matrix*:\n", |
||
| 4177 | "\n", |
||
| 4178 | " \n", |
||
| 4179 | "\n", |
||
| 4180 | "False positive: when the model predicts 1 but the real data is 0; i.e. a patient who would not need an ICU bed will occupy one; the impact of this error would unnecessarily increase the workload in ICU, reducing resources for those who really need it.\n", |
||
| 4181 | "\n", |
||
| 4182 | "False negative: when the model predicts 0 and the real data is 1; i.e. a patient who would need an ICU bed is being sent home; the impact of this error puts the patient's life at risk as their health may worsen versus receiving treatment in ICU at the appropriate time." |
||
| 4183 | ], |
||
| 4184 | "metadata": { |
||
| 4185 | "id": "EQzowsOmnvK3" |
||
| 4186 | } |
||
| 4187 | }, |
||
| 4188 | { |
||
| 4189 | "cell_type": "code", |
||
| 4190 | "source": [ |
||
| 4191 | "score_window_0 = log_reg_model(model_data)" |
||
| 4192 | ], |
||
| 4193 | "metadata": { |
||
| 4194 | "id": "I9v_yS81CQFk", |
||
| 4195 | "colab": { |
||
| 4196 | "base_uri": "https://localhost:8080/", |
||
| 4197 | "height": 725 |
||
| 4198 | }, |
||
| 4199 | "outputId": "d683d8dc-69ef-4dd1-8777-60fe0ca73729" |
||
| 4200 | }, |
||
| 4201 | "execution_count": 31, |
||
| 4202 | "outputs": [ |
||
| 4203 | { |
||
| 4204 | "output_type": "stream", |
||
| 4205 | "name": "stdout", |
||
| 4206 | "text": [ |
||
| 4207 | "Training data: (1347, 231)\n", |
||
| 4208 | "Test data: (578, 231)\n", |
||
| 4209 | "\n" |
||
| 4210 | ] |
||
| 4211 | }, |
||
| 4212 | { |
||
| 4213 | "output_type": "stream", |
||
| 4214 | "name": "stderr", |
||
| 4215 | "text": [ |
||
| 4216 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", |
||
| 4217 | " y = column_or_1d(y, warn=True)\n" |
||
| 4218 | ] |
||
| 4219 | }, |
||
| 4220 | { |
||
| 4221 | "output_type": "stream", |
||
| 4222 | "name": "stdout", |
||
| 4223 | "text": [ |
||
| 4224 | " precision recall f1-score support\n", |
||
| 4225 | "\n", |
||
| 4226 | " 0 0.92 0.94 0.93 423\n", |
||
| 4227 | " 1 0.82 0.79 0.80 155\n", |
||
| 4228 | "\n", |
||
| 4229 | " accuracy 0.90 578\n", |
||
| 4230 | " macro avg 0.87 0.86 0.87 578\n", |
||
| 4231 | "weighted avg 0.90 0.90 0.90 578\n", |
||
| 4232 | "\n", |
||
| 4233 | "ROC_AUC_Score: 0.8616334934797529\n" |
||
| 4234 | ] |
||
| 4235 | }, |
||
| 4236 | { |
||
| 4237 | "output_type": "display_data", |
||
| 4238 | "data": { |
||
| 4239 | "text/plain": [ |
||
| 4240 | "<Figure size 700x500 with 2 Axes>" |
||
| 4241 | ], |
||
| 4242 | "image/png": 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\n" |
||
| 4243 | }, |
||
| 4244 | "metadata": {} |
||
| 4245 | } |
||
| 4246 | ] |
||
| 4247 | }, |
||
| 4248 | { |
||
| 4249 | "cell_type": "code", |
||
| 4250 | "source": [ |
||
| 4251 | "score_window_0 = decision_tree_model(model_data)" |
||
| 4252 | ], |
||
| 4253 | "metadata": { |
||
| 4254 | "id": "hYIOELJkCsa-", |
||
| 4255 | "colab": { |
||
| 4256 | "base_uri": "https://localhost:8080/", |
||
| 4257 | "height": 671 |
||
| 4258 | }, |
||
| 4259 | "outputId": "7542c823-1eaa-4095-f5d4-c7386d9c28b0" |
||
| 4260 | }, |
||
| 4261 | "execution_count": 32, |
||
| 4262 | "outputs": [ |
||
| 4263 | { |
||
| 4264 | "output_type": "stream", |
||
| 4265 | "name": "stdout", |
||
| 4266 | "text": [ |
||
| 4267 | "Training data: (1347, 231)\n", |
||
| 4268 | "Test data: (578, 231)\n", |
||
| 4269 | "\n", |
||
| 4270 | " precision recall f1-score support\n", |
||
| 4271 | "\n", |
||
| 4272 | " 0 0.94 0.93 0.93 423\n", |
||
| 4273 | " 1 0.81 0.84 0.83 155\n", |
||
| 4274 | "\n", |
||
| 4275 | " accuracy 0.90 578\n", |
||
| 4276 | " macro avg 0.88 0.88 0.88 578\n", |
||
| 4277 | "weighted avg 0.91 0.90 0.91 578\n", |
||
| 4278 | "\n", |
||
| 4279 | "ROC_AUC_Score: 0.883893845801876\n" |
||
| 4280 | ] |
||
| 4281 | }, |
||
| 4282 | { |
||
| 4283 | "output_type": "display_data", |
||
| 4284 | "data": { |
||
| 4285 | "text/plain": [ |
||
| 4286 | "<Figure size 700x500 with 2 Axes>" |
||
| 4287 | ], |
||
| 4288 | "image/png": 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srL1atWqt4uJiFBFhkyTl55+o9pqzZ8+pqOiMgoODCC/XGIvc9+y06ooKVqu12uX+P1VRUaEPP/xQJSUlSkhI0LZt21ReXu4yNaFdu3Zq0aKFM8BkZmYqLi7OpaWUlJSk0aNHKysrq1aV+csKMAMHDtSmTZv097//XYsXL5YkRUVFafPmzTV68bS0NI0bN85lHxWYK8N3330n6cKE259bt26d1q1b57JvyJAhkqTVq1df9usQYIALrrsuRJJ0+vSFVX+tWrWUn5+fTp/+Xvn5J6pM5t2z5xtJUps2N9XrOOF53paqLfvLVV1R4ZlnnnE+s+vndu3apYSEBJWWlqphw4ZatGiRoqOjtWPHDvn5+SkkJMTl/PDwcOXlXZiQnpeX5xJefjj+w7HauOwvc+zSpYvmzZt3WdfWJNnBM37zm99IkrKzs2t0fmpqqiTpzTffrPFrNGrUSJ06dVJlZSXhBfiJLVu2SZJatGgmSWrQoIHi42/Rl1+u19KlX2jYsAddzv/88y8kSXfccVv9DhRXleqKCr/0O7pt27basWOHCgsL9dFHH2nYsGFas2ZNXQ+zilrN3vHy8pK3t/cvbrWdRYz6deuttyopKanKVzf4+PhozJgxGjJkiM6ePav333/feax58+ZVKjINGjTQG2+8ofj4eM2ePVtbtmxxOT5ixAjnEuufioyMVEZGhoKCgvTpp586l2kD14KvvtqhL7/cUGXCe3l5ud59d77+/e//qEEDq5KTezuPDR8+WJI0a9a/dOjQjw+T3L59p95/f6GCghpp4MC762X8uHJ4qdJtm9VqVVBQkMv2SwHGz89PN954o7p06aL09HR16NBBL7/8smw2m8rKyqp8x11+fr5stgvtUJvNpvz8/CrHfzhWG7VKG4sWLbrosczMTL3yyivVrkTBleOmm27SnDlzdPLkSW3btk2nTp1SkyZNFBcXp8jISJ07d07Dhw/X0aNHndf07NlTb775prZu3aqcnBz5+/urW7duaty4sZYuXarRo0dXeZ2HHnpI//rXv5SVlaVvvvlG5eXlat68ubp06aIGDRpo9+7dGjVqVH2+dcDjDh/O0dNPP6frrgtRTEyUQkKC9f33Bfr22wM6efI7Wa1WTZnyrHPuiyTdemu8hgwZpHfeeU8pKYN0663xKis7r8zMjXI4pL/97Rm+B+ka5OXGFtKvVVlZKbvdri5dusjX11crVqzQwIEDJUn79u1TTk6OEhISJEkJCQn629/+phMnTji/zHT58uUKCgpyfoVNTf3qL3Pct2+f/ud//keffPKJBg8erMmTJ1/0oWe/OBC+zLFeXH/99fr973+v3/zmN7rhhhvUpEkTlZWV6dChQ1q5cqVeeeWVKu2j2NhYPf300+ratatsNpvsdrt27dql2bNna/bs2dW+Tt++fZWSkqKuXbsqIiJCjRo1UmFhoXbv3q0FCxbojTfe4KsE6hmrkDzv6NFj+uijxdqyZZuOHDmmgoIC+fr6KjIyUl273qyHHhqkli2bV3vtokX/1rx5H+h///egfH191aFDnEaP/r06darZU7BR9+pzFdJ9UZ+77V4f7u196ZP+T1pampKTk9WiRQudOXNGGRkZmjZtmpYtW6Y777xTo0eP1n/+8x/NmTNHQUFBeuyxxyRd+A4+6cLE344dOyoyMlLTp09XXl6ehgwZot///veaMmVKrcZ92QHm+PHjeuaZZzR37lwlJSUpPT1dsbGxl3OrCwMhwAB1igAD1K36DDAPRC11273m7+1T43NHjBihFStWKDc3V8HBwWrfvr0mTJigO++8U9KPD7J77733XB5k99P20OHDhzV69GitXr1agYGBGjZsmKZOnVrrKSi1DjCFhYWaMmWKZsyYoY4dO2ratGm67bZfP4GMAAPULQIMULfqM8A8GPUft90rY29ft92rPtUq7kyfPl3Tpk2TzWbTe++9V6tvIAYAAHCXWlVgvLy85O/vr8TEROeXjVVn4cKFtR8IFRigTlGBAepWfVZgHope4rZ7vbvnLrfdqz7VqgIzdOhQggYAAB7mrStnFZKn1CrAzJkzp46GAQAAUHM8dQ4AAMNcSc+B8RQCDAAAhqGFVMuvEgAAALgSUIEBAMAwXlRgCDAAAJjGmzkwtJAAAIB5qMAAAGAYWkgEGAAAjMMyalpIAADAQFRgAAAwDM+BIcAAAGAc5sDQQgIAAAaiAgMAgGF4DgwBBgAA49BCooUEAAAMRAUGAADD8BwYAgwAAMZhGTUtJAAAYCAqMAAAGIZJvAQYAACMwzJqWkgAAMBAVGAAADAMLSQCDAAAxmEZNS0kAABgICowAAAYhufAEGAAADAOc2BoIQEAAANRgQEAwDBM4iXAAABgHObA0EICAAAGogIDAIBhaCERYAAAMA4tJFpIAADAQFRgAAAwDM+BIcAAAGAcb+bA0EICAADmoQIDAIBhvFTh6SF4HBUYAAAM422pdNtWG+np6brlllvUqFEjhYWF6e6779a+fftczrn99ttlsVhctkcffdTlnJycHPXr108BAQEKCwvTk08+qfPnz9dqLFRgAABAjaxZs0apqam65ZZbdP78eT399NPq3bu39uzZo8DAQOd5I0eO1OTJk50/BwQEOP9cUVGhfv36yWazacOGDcrNzdXQoUPl6+urKVOm1HgsBBgAAAzj46EW0tKlS11+njNnjsLCwrRt2zb16NHDuT8gIEA2m63ae3z++efas2ePvvjiC4WHh6tjx476y1/+ogkTJujZZ5+Vn59fjcZCCwkAAMN4q8Jtm91uV1FRkctmt9trNI7CwkJJUmhoqMv+efPmqUmTJoqNjVVaWprOnj3rPJaZmam4uDiFh4c79yUlJamoqEhZWVk1/gwIMAAAXMPS09MVHBzssqWnp1/yusrKSj3++OPq1q2bYmNjnfsffPBBvfvuu1q1apXS0tL0zjvv6KGHHnIez8vLcwkvkpw/5+Xl1XjctJAAADCMt8V9LaS0tDSNGzfOZZ/Var3kdampqdq9e7fWrVvnsn/UqFHOP8fFxSkiIkK9evVSdna2Wrdu7Z5BiwADAIBxvN04B8ZqtdYosPzUmDFjtGTJEq1du1bNmjX7xXPj4+MlSQcOHFDr1q1ls9m0efNml3Py8/Ml6aLzZqpDCwkAANSIw+HQmDFjtGjRIq1cuVKtWrW65DU7duyQJEVEREiSEhIStGvXLp04ccJ5zvLlyxUUFKTo6Ogaj4UKDAAAhvHUVwmkpqYqIyNDH3/8sRo1auScsxIcHCx/f39lZ2crIyNDffv2VePGjbVz506NHTtWPXr0UPv27SVJvXv3VnR0tIYMGaLp06crLy9PEydOVGpqaq0qQRaHw+Gok3dZSxaLxdNDAK5qFRVnPD0E4Krm5dWw3l7r3/Fj3Hav3256tcbnXux39ezZszV8+HAdOXJEDz30kHbv3q2SkhI1b95c99xzjyZOnKigoCDn+YcPH9bo0aO1evVqBQYGatiwYZo6dap8fGpeVyHAANcIAgxQt66FAHMloYUEAIBh3DmJ11QEGAAADOPOZdSmYhUSAAAwDhUYAAAMQwuJAAMAgHE8tYz6SkILCQAAGIcKDAAAhvGhhUSAAQDANMyBoYUEAAAMRAUGAADD8BwYAgwAAMahhUQLCQAAGIgKDAAAhuE5MAQYAACMwzJqWkgAAMBAVGAAADAMk3gJMAAAGIdl1LSQAACAgajAAABgGFpIBBgAAIzDMmpaSAAAwEBUYAAAMAzPgSHAAABgHObA0EICAAAGogIDAIBheA4MAQYAAOPQQqKFBAAADEQFBgAAw/AcGAIMAADGYRk1LSQAAGAgKjAAABiGSbwEGAAAjMMyalpIAADAQFRgAAAwDC0kAgwAAMZhGTUtJAAAYKArpgLjcDg8PQTgqpa7sLGnhwBc1SJSTtXba3lRgblyAgwAAKgZh5f7AozFbXeqX7SQAACAcajAAABgGIeX+6ZdmFqBIcAAAGAYdwYYU9FCAgAANZKenq5bbrlFjRo1UlhYmO6++27t27fP5ZzS0lKlpqaqcePGatiwoQYOHKj8/HyXc3JyctSvXz8FBAQoLCxMTz75pM6fP1+rsRBgAAAwjMPL4batNtasWaPU1FRt3LhRy5cvV3l5uXr37q2SkhLnOWPHjtUnn3yiDz/8UGvWrNHx48eVkpLiPF5RUaF+/fqprKxMGzZs0Ny5czVnzhxNmjSpVmOxOFi/DFwTWEYN1K36XEZ9/q5WbruXz5KDl33tyZMnFRYWpjVr1qhHjx4qLCxU06ZNlZGRoXvvvVeS9M033ygqKkqZmZnq2rWrPvvsM9111106fvy4wsPDJUmzZs3ShAkTdPLkSfn5+dXotanAAABwDbPb7SoqKnLZ7HZ7ja4tLCyUJIWGhkqStm3bpvLyciUmJjrPadeunVq0aKHMzExJUmZmpuLi4pzhRZKSkpJUVFSkrKysGo+bAAMAgGEcXpVu29LT0xUcHOyypaenX3IMlZWVevzxx9WtWzfFxsZKkvLy8uTn56eQkBCXc8PDw5WXl+c856fh5YfjPxyrKVYhAQBgGHeuQkpLS9O4ceNc9lmt1ktel5qaqt27d2vdunVuG0ttEGAAALiGWa3WGgWWnxozZoyWLFmitWvXqlmzZs79NptNZWVlKigocKnC5Ofny2azOc/ZvHmzy/1+WKX0wzk1QQsJAADDeGoVksPh0JgxY7Ro0SKtXLlSrVq5Tibu0qWLfH19tWLFCue+ffv2KScnRwkJCZKkhIQE7dq1SydOnHCes3z5cgUFBSk6OrrGY6ECAwCAYTz1ILvU1FRlZGTo448/VqNGjZxzVoKDg+Xv76/g4GCNGDFC48aNU2hoqIKCgvTYY48pISFBXbt2lST17t1b0dHRGjJkiKZPn668vDxNnDhRqamptaoEsYwauEawjBqoW/W5jLr03maXPqmGGnx0tMbnWizVf/HA7NmzNXz4cEkXHmT3xBNP6L333pPdbldSUpJmzpzp0h46fPiwRo8erdWrVyswMFDDhg3T1KlT5eNT87oKAQa4RhBggLpVrwHm/v9y270afHDMbfeqT7SQAAAwjMOr0tND8Dgm8QIAAONQgQEAwDB8GzUBBgAA4xBgaCEBAAADUYEBAMAwVGAIMAAAmIcAQwsJAACYhwoMAACG4TkwBBgAAIzDHBhaSAAAwEBUYAAAMAwVGAIMAADGIcDQQgIAAAaiAgMAgGmowBBgAAAwDcuoaSEBAAADUYEBAMAwTOIlwAAAYBwCDC0kAABgICowAAAYhgoMAQYAAPMQYGghAQAA81CBAQDAMDwHhgADAIBxmANDCwkAABiICgwAAIahAkOAAQDAOAQYWkgAAMBAVGAAADANFRgCDAAApmEZNS0kAABgICowAACYxsvi6RF4HAEGAADT0D/hIwAAAOahAgMAgGloIRFgAAAwDgGGFhIAADAPFRgAAExDBYYAAwCAcQgwtJAAAIB5qMAAAGAayg98BAAAGMfL4r6tFtauXav+/fsrMjJSFotFixcvdjk+fPhwWSwWl61Pnz4u55w+fVqDBw9WUFCQQkJCNGLECBUXF9f+I6j1FQAA4JpUUlKiDh066J///OdFz+nTp49yc3Od23vvvedyfPDgwcrKytLy5cu1ZMkSrV27VqNGjar1WGghAQBgGg9N4k1OTlZycvIvnmO1WmWz2ao9tnfvXi1dulRbtmzRzTffLEmaMWOG+vbtqxdeeEGRkZE1HgsVGAAATOPGFpLdbldRUZHLZrfbL3toq1evVlhYmNq2bavRo0fr1KlTzmOZmZkKCQlxhhdJSkxMlJeXlzZt2lS7j+CyRwgAAIyXnp6u4OBgly09Pf2y7tWnTx+9/fbbWrFihaZNm6Y1a9YoOTlZFRUVkqS8vDyFhYW5XOPj46PQ0FDl5eXV6rVoIQEAYBo3tpDS0tI0btw4l31Wq/Wy7vXAAw84/xwXF6f27durdevWWr16tXr16vWrxvlzBBgAAEzjxv6J1Wq97MByKTfccIOaNGmiAwcOqFevXrLZbDpx4oTLOefPn9fp06cvOm/mYmghAQCAOnH06FGdOnVKERERkqSEhAQVFBRo27ZtznNWrlypyspKxcfH1+reVGAAADCNh1YhFRcX68CBA86fDx48qB07dig0NFShoaF67rnnNHDgQNlsNmVnZ+upp57SjTfeqKSkJElSVFSU+vTpo5EjR2rWrFkqLy/XmDFj9MADD9RqBZJEBQYAAPN46EF2W7duVadOndSpUydJ0rhx49SpUydNmjRJ3t7e2rlzp37729+qTZs2GjFihLp06aIvv/zSpUU1b948tWvXTr169VLfvn3VvXt3vfHGG7X+CCwOh8NR66sAGCd3YWNPDwG4qkWknLr0SW6S+6+GbrtXxO9r/xTcKwEtJAAATMO3URNgAAAwDgGGOTAAAMA8VGAAADAN5QcCDAAAxqGFRIYDAADmoQIDAIBpqMAQYAAAMA4BhhYSAAAwDxUYAABMQ/mBAAMAgHFoIZHhAACAeajAAABgGiowBBgAAIxDgKGFBAAAzEMFBgAA01CBoQIDAADMQwUGAADTUIGpfYA5d+6cHA6HAgICJEmHDx/WokWLFB0drd69e7t9gKgf586d0/r167Vy5Upt27ZNx48fl7e3t1q0aKHevXvr4YcfVmBgoMs1M2bM0KuvvnrRe44cOVLjx4+v66EDV5x9x7y1db+fvjnqo71HfPRdkbckaXX6d9Wev36Pn9bu9tO3x3106oyXSkotauTvUNv/Oq8BXc/p1qjyi77WrkM+endVgPYc8VF5hUXXh53XPQmlSupsr5P3hisE/ZPaB5gBAwYoJSVFjz76qAoKChQfHy9fX1999913+vvf/67Ro0fXxThRx5YsWaKJEydKklq3bq2ePXuquLhY27dv14wZM/Tpp5/q3XffVePGjatc27lzZ7Vs2bLK/piYmDofN3AlentlgNbvsdb4/GXbrfoyy0/Xh1Uouvl5+VsdyvveS5u+9dOmb/00+PazGpl0tsp1a3b76bn3GsnhkNpff17BgZX66oCv0j9spOw8b/2xb9VrgKtFrQPMV199pZdeekmS9NFHHyk8PFzbt2/XggULNGnSJAKMoXx8fPS73/1Ow4YNU+vWrZ37T5w4oT/84Q/as2ePpkyZohdffLHKtffdd59SUlLqc7jAFS2mxXm1tlWoXbPzatusXA9MD1X5+YuX/IfccVZP3F2s4ECHy/49OT564q0gZazxV68Odt1gq3AeKzpr0fSPGqqy0qLJg4vUI7ZMknT6jEWPvR6iD74MUEK7cnW64eLVGxiMFlLti1Bnz55Vo0aNJEmff/65UlJS5OXlpa5du+rw4cNuHyDqxz333KPJkye7hBdJCgsL06RJkyRd+OddVlbmieEBRnnwN+f0yJ1ndWtUmRo3clzy/JsiK6qEF0mKbnFed7Qvk8Nh0fZsX5djn25poBK7l7pF253hRZJCGzn0aHKJJOmDL/1/5TvBFcvL4r7NULUOMDfeeKMWL16sI0eOaNmyZc55LydOnFBQUJDbBwjPa9eunSSprKxMBQUFnh0McI3x8b4QbHx+Vi/fuM9PkvSb2Kr/UdG1bZn8fBzadsBXdgowuErVuoU0adIkPfjggxo7dqx69uyphIQESRf+67xTp05uHyA878iRI5IkX19fhYSEVDm+ceNG7d27V3a7XTabTT169FBsbGw9jxK4+vxvnrdW7bTKx9uhm290DSoHci9MDG4Teb7Kdb4+Uqvw89p3zFdHv/NW64iKKufAcAZXTtyl1gHm3nvvVffu3ZWbm6sOHTo49/fq1Uv33HOPWweHK8Pbb78tSerevbv8/PyqHP/4449dfn755ZeVlJSk9PT0KiuXAFzchr1+WrPbTxUVUn6ht7IO+8jbWxp/T7H+q3Gl87ySUotKSi8U0JsGV1Z7r6bBldp3TMovIMBclQgwl/ccGJvNpuLiYi1fvlw9evSQv7+/brnlFlksfKBXmzVr1uijjz6Sr6+vHn/8cZdjLVq00IQJE9SjRw9FRkaqqKhIW7Zs0fPPP69ly5apoqJC//znPz0zcMBAB3K9teyrBs6frb4OPXZXsXp3cl0Sfa7M4nJOdRr4Xdh/1s6/l3F1qnWAOXXqlO6//36tWrVKFotF+/fv1w033KARI0bouuuuq3aVys/Z7XbZ7a5/Ia1Wq6zWmi87RN3Lzs7Wk08+KYfDoSeffNI5F+YHAwYMcPk5ICBA/fv3V3x8vPr3768vvvhCO3bsUMeOHetx1IC5hvY8p6E9z8leLh075a1/b2qgFxY10vq9Vk0eXCRfHj2KH/AcmNp/BGPHjpWvr69ycnKcD7OTpN/97ndaunRpje6Rnp6u4OBgly09Pb22Q0Edys/P18iRI1VYWKiHH35Yw4YNq/G1YWFhzmXVX375ZV0NEbhqWX2lG2wVenxAiVISzinzGz8tzPxxRZG/349VF3t59RWW0v+r0gRYL70KCgayeLtvM1StA8znn3+uadOmqVmzZi77b7rpphovo05LS1NhYaHLlpaWVtuhoI4UFBTokUce0bFjx5SSkqIJEybU+h7XX3+9JOnkyZNuHh1wbfmhfbR+z4/zzwIbOBTY4MLcl5OF1f9r/If94SHMf8HVqdYFyZKSEpfKyw9Onz5d4xYQ7aIrV0lJiUaOHKkDBw6od+/e+utf/3pZc5sKCwslSf7+PIcC+DWCAy8ElYIS17+HN0ZU6OuDXvr2uI+uD3cNKecrpIP5PvLzcahZEwLMVcngyom71LgCc/z4cUnSbbfd5lyVIkkWi0WVlZWaPn267rjjDvePEPWmrKxMf/zjH7Vz5051795dL774ory9a/+XxOFw6IsvvpAkRUdHu3uYwDVlx8ELD7CLDHVdbdS17YVl1Wt2V10ZmPmNn8rOW9TlxnJZfascxtXAy9t9m6FqHGBiYmKUkZGh559/Xm+88YaSk5NVVlamp556SrGxsVq7dq2mTZtWl2NFHaqoqNC4ceO0ceNG3XzzzXr11VerXTL9g9OnT2vevHkqLi522V9SUqJnnnlGX3/9tZo2bcoXfAKXUFBs0ZLNVpVW85Drrft99fpnFx5FkHxzqcuxfreUKtBaqfV7rFr7kxDzfbFFs/7vmvtvO1d3Awc8zOJwOGo0w2vmzJmaMGGC+vTpo1mzZmnWrFn6+uuvVVxcrM6dOys1NVURERF1PV7Ukblz52rKlCmSpDvvvFMNGzas9rynnnpKoaGhOnr0qHr16qWAgADFxcWpadOm+v7775WVlaWCggIFBQVp1qxZ6tKlS32+DfyC3IVVv4gTdSPzG1+9vfLHVvs3R33kcFgU1fzHx+IO7XlWCe3Klfu9lwZND1UDX4fa/Nd5NQ2uUGmZRUe+81bOyQtd/vu6nVPqXSVVXmfNbj89l9FIDkkdW5UrKODC03eLS710f/dz+mO/qteg7kSknKq318pd38Zt94ro9q3b7lWfajwH5o9//KOSk5M1YsQIxcTE6I033tCf/vSnuhwb6lFRUZHzz8uXL7/oeWPGjFFoaKhCQkI0cuRIff311zp06JC2b98ub29vNWvWTCkpKRo+fLjCw8PrY+jAFaegxEt7j1Tt3fx0X0HJhQL4dYGVejS5RDv+11cH872175iPKh1S40aV6tnerv7xpRf9QsbfxJbp5VGFemdVgPbk+Oh8hUUtw87rnoQS9elir/YaXCUsrKmvcQXmp1599VWNHTtWUVFR8vnZF3R89dVXbhscAPehAgPUrXqtwGxw3/zCiFv3uO1e9anWEe7w4cNauHChrrvuOg0YMKBKgAEAAHWMVUi1CzBvvvmmnnjiCSUmJiorK0tNmzatq3EBAICLIcDUPMD06dNHmzdv1quvvqqhQ4fW5ZgAAAB+UY0DTEVFhXbu3FnlCbwAAKCeGfz8FnepcYD5pZUpAACgHtFC4vssAQCAeQgwAACYxuLjvq0W1q5dq/79+ysyMlIWi0WLFy92Oe5wODRp0iRFRETI399fiYmJ2r9/v8s5p0+f1uDBgxUUFKSQkBCNGDGiylPda4IAAwCAaSze7ttqoaSkRB06dNA///nPao9Pnz5dr7zyimbNmqVNmzYpMDBQSUlJKi398aswBg8erKysLC1fvlxLlizR2rVrNWrUqNp/BJfzIDsA5uFBdkDdqtcH2W27zW33iujy5WVdZ7FYtGjRIt19992SLlRfIiMj9cQTT2j8+PGSpMLCQoWHh2vOnDl64IEHtHfvXkVHR2vLli26+eabJUlLly5V3759dfToUUVGRtb49anAAABgGjdWYOx2u4qKilw2u732X0Vx8OBB5eXlKTEx0bkvODhY8fHxyszMlCRlZmYqJCTEGV4kKTExUV5eXtq0aVOtXo8AAwCAaby83balp6crODjYZUtPT6/1kPLy8iSpyvfghYeHO4/l5eUpLCzM5biPj49CQ0Od59QU3wMAAMA1LC0tTePGjXPZZ7VaPTSamiPAAABgGjd+G7XVanVLYLHZbJKk/Px8RUREOPfn5+erY8eOznNOnDjhct358+d1+vRp5/U1RQsJAADTeGgV0i9p1aqVbDabVqxY4dxXVFSkTZs2KSEhQZKUkJCggoICbdu2zXnOypUrVVlZqfj4+Fq9HhUYAABQI8XFxTpw4IDz54MHD2rHjh0KDQ1VixYt9Pjjj+uvf/2rbrrpJrVq1Up//vOfFRkZ6VypFBUVpT59+mjkyJGaNWuWysvLNWbMGD3wwAO1WoEkEWAAADCPh75KYOvWrbrjjjucP/8wd2bYsGGaM2eOnnrqKZWUlGjUqFEqKChQ9+7dtXTpUjVo0MB5zbx58zRmzBj16tVLXl5eGjhwoF555ZVaj4XnwADXCJ4DA9Sten0OzO673XaviNjFbrtXfWIODAAAMA4tJAAATOPFt1ETYAAAMI0bl1GbihYSAAAwDhEOAADTeGgV0pWEAAMAgGkIMLSQAACAeajAAABgGiowBBgAAIzDMmpaSAAAwDxUYAAAMA3PgSHAAABgHObA0EICAADmoQIDAIBpqMAQYAAAMA4BhhYSAAAwDxUYAABMw3NgCDAAABiHZdS0kAAAgHmIcAAAmIZJvAQYAACMQ4ChhQQAAMxDBQYAANNQgSHAAABgHFYh0UICAADmIcIBAGAYCxUYAgwAAKYhwNBCAgAABiLCAQBgGCowBBgAAAzEr29aSAAAwDhEOAAADEMLiQADAIBxCDC0kAAAgIGIcAAAGIYKDAEGAADjEGBoIQEAAAMR4QAAMA6/vvkEAAAwDC0kWkgAAMBARDgAAAxDBYYKDAAAxrFYfNy21cazzz4ri8XisrVr1855vLS0VKmpqWrcuLEaNmyogQMHKj8/391vXxIBBgAA1EJMTIxyc3Od27p165zHxo4dq08++UQffvih1qxZo+PHjyslJaVOxkENCgAAw3iyheTj4yObzVZlf2Fhod566y1lZGSoZ8+ekqTZs2crKipKGzduVNeuXd06DiowAAAYx8dtm91uV1FRkctmt9sv+sr79+9XZGSkbrjhBg0ePFg5OTmSpG3btqm8vFyJiYnOc9u1a6cWLVooMzPTze+fAAMAwDUtPT1dwcHBLlt6enq158bHx2vOnDlaunSpXnvtNR08eFC33Xabzpw5o7y8PPn5+SkkJMTlmvDwcOXl5bl93LSQAAAwjDtbSGlpaRo3bpzLPqvVWu25ycnJzj+3b99e8fHxatmypT744AP5+/u7bUw1QYABAMAw7gwwVqv1ooHlUkJCQtSmTRsdOHBAd955p8rKylRQUOBShcnPz692zsyvRQsJAABcluLiYmVnZysiIkJdunSRr6+vVqxY4Ty+b98+5eTkKCEhwe2vTQUGAADDeGoV0vjx49W/f3+1bNlSx48f1zPPPCNvb28NGjRIwcHBGjFihMaNG6fQ0FAFBQXpscceU0JCgttXIEkEGAAAjOOpAHP06FENGjRIp06dUtOmTdW9e3dt3LhRTZs2lSS99NJL8vLy0sCBA2W325WUlKSZM2fWyVgsDofDUSd3BnBFyV3Y2NNDAK5qESmn6u21zp7d6rZ7BQTc7LZ71ScqMAAAGIdf33wCAAAYhi9zZBUSAAAwEBEOAADDUIEhwAAAYBwCDC0kAABgICIcAACGoQJDgAEAwED8+qaFBAAAjEOEAwDAMLSQCDAAABiHAEMLCQAAGIgIBwCAYajAEGAAADAOAYYWEgAAMBARDgAA4/Drm08AAADD0EKihQQAAAxEhAMAwDBUYCSLw+FweHoQMIvdbld6errS0tJktVo9PRzgqsPfMeDSCDCotaKiIgUHB6uwsFBBQUGeHg5w1eHvGHBpzIEBAADGIcAAAADjEGAAAIBxCDCoNavVqmeeeYbJhUAd4e8YcGlM4gUAAMahAgMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGFRr+PDhslgsmjp1qsv+xYsXy2KxeGhUgNkcDocSExOVlJRU5djMmTMVEhKio0ePemBkgHkIMLioBg0aaNq0afr+++89PRTgqmCxWDR79mxt2rRJr7/+unP/wYMH9dRTT2nGjBlq1qyZB0cImIMAg4tKTEyUzWZTenr6Rc9ZsGCBYmJiZLVadf311+vFF1+sxxEC5mnevLlefvlljR8/XgcPHpTD4dCIESPUu3dvderUScnJyWrYsKHCw8M1ZMgQfffdd85rP/roI8XFxcnf31+NGzdWYmKiSkpKPPhuAM8hwOCivL29NWXKFM2YMaPasva2bdt0//3364EHHtCuXbv07LPP6s9//rPmzJlT/4MFDDJs2DD16tVLjzzyiF599VXt3r1br7/+unr27KlOnTpp69atWrp0qfLz83X//fdLknJzczVo0CA98sgj2rt3r1avXq2UlBTxLFJcq3gSL6o1fPhwFRQUaPHixUpISFB0dLTeeustLV68WPfcc48cDocGDx6skydP6vPPP3de99RTT+nTTz9VVlaWB0cPXPlOnDihmJgYnT59WgsWLNDu3bv15ZdfatmyZc5zjh49qubNm2vfvn0qLi5Wly5ddOjQIbVs2dKDIweuDFRgcEnTpk3T3LlztXfvXpf9e/fuVbdu3Vz2devWTfv371dFRUV9DhEwTlhYmP7whz8oKipKd999t77++mutWrVKDRs2dG7t2rWTJGVnZ6tDhw7q1auX4uLidN999+nNN99kfhquaQQYXFKPHj2UlJSktLQ0Tw8FuKr4+PjIx8dHklRcXKz+/ftrx44dLtv+/fvVo0cPeXt7a/ny5frss88UHR2tGTNmqG3btjp48KCH3wXgGT6eHgDMMHXqVHXs2FFt27Z17ouKitL69etdzlu/fr3atGkjb2/v+h4iYLTOnTtrwYIFuv76652h5ucsFou6deumbt26adKkSWrZsqUWLVqkcePG1fNoAc+jAoMaiYuL0+DBg/XKK6849z3xxBNasWKF/vKXv+jbb7/V3Llz9eqrr2r8+PEeHClgptTUVJ0+fVqDBg3Sli1blJ2drWXLlunhhx9WRUWFNm3apClTpmjr1q3KycnRwoULdfLkSUVFRXl66IBHEGBQY5MnT1ZlZaXz586dO+uDDz7Q/PnzFRsbq0mTJmny5MkaPny45wYJGCoyMlLr169XRUWFevfurbi4OD3++OMKCQmRl5eXgoKCtHbtWvXt21dt2rTRxIkT9eKLLyo5OdnTQwc8glVIAADAOFRgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGCc/w8bxmH4xv/KDAAAAABJRU5ErkJggg==\n" |
||
| 4289 | }, |
||
| 4290 | "metadata": {} |
||
| 4291 | } |
||
| 4292 | ] |
||
| 4293 | }, |
||
| 4294 | { |
||
| 4295 | "cell_type": "code", |
||
| 4296 | "source": [ |
||
| 4297 | "score_window_0 = k_neighbors_model(model_data)" |
||
| 4298 | ], |
||
| 4299 | "metadata": { |
||
| 4300 | "id": "LEUOWmitCyi5", |
||
| 4301 | "colab": { |
||
| 4302 | "base_uri": "https://localhost:8080/", |
||
| 4303 | "height": 725 |
||
| 4304 | }, |
||
| 4305 | "outputId": "92a57341-378b-422e-9c69-6596e3b41d25" |
||
| 4306 | }, |
||
| 4307 | "execution_count": 33, |
||
| 4308 | "outputs": [ |
||
| 4309 | { |
||
| 4310 | "output_type": "stream", |
||
| 4311 | "name": "stdout", |
||
| 4312 | "text": [ |
||
| 4313 | "Training data: (1347, 231)\n", |
||
| 4314 | "Test data: (578, 231)\n", |
||
| 4315 | "\n" |
||
| 4316 | ] |
||
| 4317 | }, |
||
| 4318 | { |
||
| 4319 | "output_type": "stream", |
||
| 4320 | "name": "stderr", |
||
| 4321 | "text": [ |
||
| 4322 | "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", |
||
| 4323 | " return self._fit(X, y)\n" |
||
| 4324 | ] |
||
| 4325 | }, |
||
| 4326 | { |
||
| 4327 | "output_type": "stream", |
||
| 4328 | "name": "stdout", |
||
| 4329 | "text": [ |
||
| 4330 | " precision recall f1-score support\n", |
||
| 4331 | "\n", |
||
| 4332 | " 0 0.78 1.00 0.87 423\n", |
||
| 4333 | " 1 1.00 0.21 0.35 155\n", |
||
| 4334 | "\n", |
||
| 4335 | " accuracy 0.79 578\n", |
||
| 4336 | " macro avg 0.89 0.61 0.61 578\n", |
||
| 4337 | "weighted avg 0.84 0.79 0.73 578\n", |
||
| 4338 | "\n", |
||
| 4339 | "ROC_AUC_Score: 0.6064516129032258\n" |
||
| 4340 | ] |
||
| 4341 | }, |
||
| 4342 | { |
||
| 4343 | "output_type": "display_data", |
||
| 4344 | "data": { |
||
| 4345 | "text/plain": [ |
||
| 4346 | "<Figure size 700x500 with 2 Axes>" |
||
| 4347 | ], |
||
| 4348 | "image/png": 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\n" |
||
| 4349 | }, |
||
| 4350 | "metadata": {} |
||
| 4351 | } |
||
| 4352 | ] |
||
| 4353 | }, |
||
| 4354 | { |
||
| 4355 | "cell_type": "markdown", |
||
| 4356 | "source": [ |
||
| 4357 | "While the K-Neighbors model has a weighted F1 score of .72, its efficiency is low at .59 and has a significant number of false negative predictions." |
||
| 4358 | ], |
||
| 4359 | "metadata": { |
||
| 4360 | "id": "WX6JjC5YwHe2" |
||
| 4361 | } |
||
| 4362 | }, |
||
| 4363 | { |
||
| 4364 | "cell_type": "code", |
||
| 4365 | "source": [ |
||
| 4366 | "# Remove patients admitted to ICU in the last window 12+ hours\n", |
||
| 4367 | "model_data = filled_data.copy()\n", |
||
| 4368 | "\n", |
||
| 4369 | "remove_window_4 = model_data[(model_data['WINDOW'] == 4) & (model_data['ICU'] == 1)].index\n", |
||
| 4370 | "model_data.drop(remove_window_4, inplace = True)\n", |
||
| 4371 | "\n", |
||
| 4372 | "model_data.head(15)" |
||
| 4373 | ], |
||
| 4374 | "metadata": { |
||
| 4375 | "id": "fcXem81cEvfo", |
||
| 4376 | "colab": { |
||
| 4377 | "base_uri": "https://localhost:8080/", |
||
| 4378 | "height": 648 |
||
| 4379 | }, |
||
| 4380 | "outputId": "fe9dd216-b6ae-46c3-ddeb-b36b21db5f65" |
||
| 4381 | }, |
||
| 4382 | "execution_count": 34, |
||
| 4383 | "outputs": [ |
||
| 4384 | { |
||
| 4385 | "output_type": "execute_result", |
||
| 4386 | "data": { |
||
| 4387 | "text/plain": [ |
||
| 4388 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 4389 | "0 0 1 5 0 \n", |
||
| 4390 | "1 0 1 5 0 \n", |
||
| 4391 | "2 0 1 5 0 \n", |
||
| 4392 | "3 0 1 5 0 \n", |
||
| 4393 | "5 1 1 8 1 \n", |
||
| 4394 | "6 1 1 8 1 \n", |
||
| 4395 | "7 1 1 8 1 \n", |
||
| 4396 | "8 1 1 8 1 \n", |
||
| 4397 | "10 2 0 0 0 \n", |
||
| 4398 | "11 2 0 0 0 \n", |
||
| 4399 | "12 2 0 0 0 \n", |
||
| 4400 | "13 2 0 0 0 \n", |
||
| 4401 | "15 3 0 3 1 \n", |
||
| 4402 | "16 3 0 3 1 \n", |
||
| 4403 | "17 3 0 3 1 \n", |
||
| 4404 | "\n", |
||
| 4405 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 4406 | "0 0.0 0.0 0.0 \n", |
||
| 4407 | "1 0.0 0.0 0.0 \n", |
||
| 4408 | "2 0.0 0.0 0.0 \n", |
||
| 4409 | "3 0.0 0.0 0.0 \n", |
||
| 4410 | "5 0.0 0.0 0.0 \n", |
||
| 4411 | "6 0.0 0.0 0.0 \n", |
||
| 4412 | "7 0.0 0.0 0.0 \n", |
||
| 4413 | "8 0.0 0.0 0.0 \n", |
||
| 4414 | "10 0.0 0.0 0.0 \n", |
||
| 4415 | "11 0.0 0.0 0.0 \n", |
||
| 4416 | "12 0.0 0.0 0.0 \n", |
||
| 4417 | "13 0.0 0.0 0.0 \n", |
||
| 4418 | "15 0.0 0.0 0.0 \n", |
||
| 4419 | "16 0.0 0.0 0.0 \n", |
||
| 4420 | "17 0.0 0.0 0.0 \n", |
||
| 4421 | "\n", |
||
| 4422 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 4423 | "0 0.0 1.0 1.0 ... \n", |
||
| 4424 | "1 0.0 1.0 1.0 ... \n", |
||
| 4425 | "2 0.0 1.0 1.0 ... \n", |
||
| 4426 | "3 0.0 1.0 1.0 ... \n", |
||
| 4427 | "5 0.0 0.0 0.0 ... \n", |
||
| 4428 | "6 0.0 0.0 0.0 ... \n", |
||
| 4429 | "7 0.0 0.0 0.0 ... \n", |
||
| 4430 | "8 0.0 0.0 0.0 ... \n", |
||
| 4431 | "10 0.0 0.0 0.0 ... \n", |
||
| 4432 | "11 0.0 0.0 0.0 ... \n", |
||
| 4433 | "12 0.0 0.0 0.0 ... \n", |
||
| 4434 | "13 0.0 0.0 0.0 ... \n", |
||
| 4435 | "15 0.0 0.0 0.0 ... \n", |
||
| 4436 | "16 0.0 0.0 0.0 ... \n", |
||
| 4437 | "17 0.0 0.0 0.0 ... \n", |
||
| 4438 | "\n", |
||
| 4439 | " OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n", |
||
| 4440 | "0 -1.000000 -1.000000 \n", |
||
| 4441 | "1 -1.000000 -1.000000 \n", |
||
| 4442 | "2 -1.000000 -1.000000 \n", |
||
| 4443 | "3 -1.000000 -1.000000 \n", |
||
| 4444 | "5 -1.000000 -1.000000 \n", |
||
| 4445 | "6 -1.000000 -1.000000 \n", |
||
| 4446 | "7 -1.000000 -1.000000 \n", |
||
| 4447 | "8 -1.000000 -0.906832 \n", |
||
| 4448 | "10 -0.959596 -0.515528 \n", |
||
| 4449 | "11 -0.959596 -0.515528 \n", |
||
| 4450 | "12 -0.959596 -0.515528 \n", |
||
| 4451 | "13 -0.797980 -0.658863 \n", |
||
| 4452 | "15 -1.000000 -1.000000 \n", |
||
| 4453 | "16 -1.000000 -1.000000 \n", |
||
| 4454 | "17 -1.000000 -1.000000 \n", |
||
| 4455 | "\n", |
||
| 4456 | " BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n", |
||
| 4457 | "0 -1.000000 -1.000000 \n", |
||
| 4458 | "1 -1.000000 -1.000000 \n", |
||
| 4459 | "2 -1.000000 -1.000000 \n", |
||
| 4460 | "3 -1.000000 -1.000000 \n", |
||
| 4461 | "5 -1.000000 -1.000000 \n", |
||
| 4462 | "6 -1.000000 -1.000000 \n", |
||
| 4463 | "7 -1.000000 -1.000000 \n", |
||
| 4464 | "8 -0.831132 -0.940967 \n", |
||
| 4465 | "10 -0.351328 -0.747001 \n", |
||
| 4466 | "11 -0.351328 -0.747001 \n", |
||
| 4467 | "12 -0.351328 -0.747001 \n", |
||
| 4468 | "13 -0.563758 -0.721834 \n", |
||
| 4469 | "15 -1.000000 -1.000000 \n", |
||
| 4470 | "16 -1.000000 -1.000000 \n", |
||
| 4471 | "17 -1.000000 -1.000000 \n", |
||
| 4472 | "\n", |
||
| 4473 | " RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 4474 | "0 -1.000000 -1.000000 \n", |
||
| 4475 | "1 -1.000000 -1.000000 \n", |
||
| 4476 | "2 -1.000000 -1.000000 \n", |
||
| 4477 | "3 -1.000000 -1.000000 \n", |
||
| 4478 | "5 -1.000000 -1.000000 \n", |
||
| 4479 | "6 -1.000000 -1.000000 \n", |
||
| 4480 | "7 -1.000000 -1.000000 \n", |
||
| 4481 | "8 -0.817204 -0.882574 \n", |
||
| 4482 | "10 -0.756272 -1.000000 \n", |
||
| 4483 | "11 -0.756272 -1.000000 \n", |
||
| 4484 | "12 -0.756272 -1.000000 \n", |
||
| 4485 | "13 -0.926882 -1.000000 \n", |
||
| 4486 | "15 -1.000000 -1.000000 \n", |
||
| 4487 | "16 -1.000000 -1.000000 \n", |
||
| 4488 | "17 -1.000000 -1.000000 \n", |
||
| 4489 | "\n", |
||
| 4490 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU ICU_SUM \n", |
||
| 4491 | "0 -1.000000 0 0 1 \n", |
||
| 4492 | "1 -1.000000 1 0 1 \n", |
||
| 4493 | "2 -1.000000 2 0 1 \n", |
||
| 4494 | "3 -1.000000 3 0 1 \n", |
||
| 4495 | "5 -1.000000 0 1 1 \n", |
||
| 4496 | "6 -1.000000 1 1 1 \n", |
||
| 4497 | "7 -1.000000 2 1 1 \n", |
||
| 4498 | "8 -1.000000 3 1 1 \n", |
||
| 4499 | "10 -0.961262 0 0 1 \n", |
||
| 4500 | "11 -0.961262 1 0 1 \n", |
||
| 4501 | "12 -0.961262 2 0 1 \n", |
||
| 4502 | "13 -0.801293 3 0 1 \n", |
||
| 4503 | "15 -1.000000 0 0 0 \n", |
||
| 4504 | "16 -1.000000 1 0 0 \n", |
||
| 4505 | "17 -1.000000 2 0 0 \n", |
||
| 4506 | "\n", |
||
| 4507 | "[15 rows x 232 columns]" |
||
| 4508 | ], |
||
| 4509 | "text/html": [ |
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| 4510 | "\n", |
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| 4511 | " <div id=\"df-67b9d01c-dc78-4361-a489-e6e610b87730\">\n", |
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| 4512 | " <div class=\"colab-df-container\">\n", |
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| 4513 | " <div>\n", |
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| 4514 | "<style scoped>\n", |
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| 4515 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 4516 | " vertical-align: middle;\n", |
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| 4517 | " }\n", |
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| 4518 | "\n", |
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| 4519 | " .dataframe tbody tr th {\n", |
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| 4520 | " vertical-align: top;\n", |
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| 4521 | " }\n", |
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| 4522 | "\n", |
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| 4523 | " .dataframe thead th {\n", |
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| 4524 | " text-align: right;\n", |
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| 4525 | " }\n", |
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| 4526 | "</style>\n", |
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| 4527 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 4528 | " <thead>\n", |
||
| 4529 | " <tr style=\"text-align: right;\">\n", |
||
| 4530 | " <th></th>\n", |
||
| 4531 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 4532 | " <th>AGE_ABOVE65</th>\n", |
||
| 4533 | " <th>AGE_PERCENTIL</th>\n", |
||
| 4534 | " <th>GENDER</th>\n", |
||
| 4535 | " <th>DISEASE GROUPING 1</th>\n", |
||
| 4536 | " <th>DISEASE GROUPING 2</th>\n", |
||
| 4537 | " <th>DISEASE GROUPING 3</th>\n", |
||
| 4538 | " <th>DISEASE GROUPING 4</th>\n", |
||
| 4539 | " <th>DISEASE GROUPING 5</th>\n", |
||
| 4540 | " <th>DISEASE GROUPING 6</th>\n", |
||
| 4541 | " <th>...</th>\n", |
||
| 4542 | " <th>OXYGEN_SATURATION_DIFF</th>\n", |
||
| 4543 | " <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n", |
||
| 4544 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
||
| 4545 | " <th>HEART_RATE_DIFF_REL</th>\n", |
||
| 4546 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
||
| 4547 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
||
| 4548 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
||
| 4549 | " <th>WINDOW</th>\n", |
||
| 4550 | " <th>ICU</th>\n", |
||
| 4551 | " <th>ICU_SUM</th>\n", |
||
| 4552 | " </tr>\n", |
||
| 4553 | " </thead>\n", |
||
| 4554 | " <tbody>\n", |
||
| 4555 | " <tr>\n", |
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| 4556 | " <th>0</th>\n", |
||
| 4557 | " <td>0</td>\n", |
||
| 4558 | " <td>1</td>\n", |
||
| 4559 | " <td>5</td>\n", |
||
| 4560 | " <td>0</td>\n", |
||
| 4561 | " <td>0.0</td>\n", |
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| 4562 | " <td>0.0</td>\n", |
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| 4563 | " <td>0.0</td>\n", |
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| 4564 | " <td>0.0</td>\n", |
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| 4565 | " <td>1.0</td>\n", |
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| 4566 | " <td>1.0</td>\n", |
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| 4567 | " <td>...</td>\n", |
||
| 4568 | " <td>-1.000000</td>\n", |
||
| 4569 | " <td>-1.000000</td>\n", |
||
| 4570 | " <td>-1.000000</td>\n", |
||
| 4571 | " <td>-1.000000</td>\n", |
||
| 4572 | " <td>-1.000000</td>\n", |
||
| 4573 | " <td>-1.000000</td>\n", |
||
| 4574 | " <td>-1.000000</td>\n", |
||
| 4575 | " <td>0</td>\n", |
||
| 4576 | " <td>0</td>\n", |
||
| 4577 | " <td>1</td>\n", |
||
| 4578 | " </tr>\n", |
||
| 4579 | " <tr>\n", |
||
| 4580 | " <th>1</th>\n", |
||
| 4581 | " <td>0</td>\n", |
||
| 4582 | " <td>1</td>\n", |
||
| 4583 | " <td>5</td>\n", |
||
| 4584 | " <td>0</td>\n", |
||
| 4585 | " <td>0.0</td>\n", |
||
| 4586 | " <td>0.0</td>\n", |
||
| 4587 | " <td>0.0</td>\n", |
||
| 4588 | " <td>0.0</td>\n", |
||
| 4589 | " <td>1.0</td>\n", |
||
| 4590 | " <td>1.0</td>\n", |
||
| 4591 | " <td>...</td>\n", |
||
| 4592 | " <td>-1.000000</td>\n", |
||
| 4593 | " <td>-1.000000</td>\n", |
||
| 4594 | " <td>-1.000000</td>\n", |
||
| 4595 | " <td>-1.000000</td>\n", |
||
| 4596 | " <td>-1.000000</td>\n", |
||
| 4597 | " <td>-1.000000</td>\n", |
||
| 4598 | " <td>-1.000000</td>\n", |
||
| 4599 | " <td>1</td>\n", |
||
| 4600 | " <td>0</td>\n", |
||
| 4601 | " <td>1</td>\n", |
||
| 4602 | " </tr>\n", |
||
| 4603 | " <tr>\n", |
||
| 4604 | " <th>2</th>\n", |
||
| 4605 | " <td>0</td>\n", |
||
| 4606 | " <td>1</td>\n", |
||
| 4607 | " <td>5</td>\n", |
||
| 4608 | " <td>0</td>\n", |
||
| 4609 | " <td>0.0</td>\n", |
||
| 4610 | " <td>0.0</td>\n", |
||
| 4611 | " <td>0.0</td>\n", |
||
| 4612 | " <td>0.0</td>\n", |
||
| 4613 | " <td>1.0</td>\n", |
||
| 4614 | " <td>1.0</td>\n", |
||
| 4615 | " <td>...</td>\n", |
||
| 4616 | " <td>-1.000000</td>\n", |
||
| 4617 | " <td>-1.000000</td>\n", |
||
| 4618 | " <td>-1.000000</td>\n", |
||
| 4619 | " <td>-1.000000</td>\n", |
||
| 4620 | " <td>-1.000000</td>\n", |
||
| 4621 | " <td>-1.000000</td>\n", |
||
| 4622 | " <td>-1.000000</td>\n", |
||
| 4623 | " <td>2</td>\n", |
||
| 4624 | " <td>0</td>\n", |
||
| 4625 | " <td>1</td>\n", |
||
| 4626 | " </tr>\n", |
||
| 4627 | " <tr>\n", |
||
| 4628 | " <th>3</th>\n", |
||
| 4629 | " <td>0</td>\n", |
||
| 4630 | " <td>1</td>\n", |
||
| 4631 | " <td>5</td>\n", |
||
| 4632 | " <td>0</td>\n", |
||
| 4633 | " <td>0.0</td>\n", |
||
| 4634 | " <td>0.0</td>\n", |
||
| 4635 | " <td>0.0</td>\n", |
||
| 4636 | " <td>0.0</td>\n", |
||
| 4637 | " <td>1.0</td>\n", |
||
| 4638 | " <td>1.0</td>\n", |
||
| 4639 | " <td>...</td>\n", |
||
| 4640 | " <td>-1.000000</td>\n", |
||
| 4641 | " <td>-1.000000</td>\n", |
||
| 4642 | " <td>-1.000000</td>\n", |
||
| 4643 | " <td>-1.000000</td>\n", |
||
| 4644 | " <td>-1.000000</td>\n", |
||
| 4645 | " <td>-1.000000</td>\n", |
||
| 4646 | " <td>-1.000000</td>\n", |
||
| 4647 | " <td>3</td>\n", |
||
| 4648 | " <td>0</td>\n", |
||
| 4649 | " <td>1</td>\n", |
||
| 4650 | " </tr>\n", |
||
| 4651 | " <tr>\n", |
||
| 4652 | " <th>5</th>\n", |
||
| 4653 | " <td>1</td>\n", |
||
| 4654 | " <td>1</td>\n", |
||
| 4655 | " <td>8</td>\n", |
||
| 4656 | " <td>1</td>\n", |
||
| 4657 | " <td>0.0</td>\n", |
||
| 4658 | " <td>0.0</td>\n", |
||
| 4659 | " <td>0.0</td>\n", |
||
| 4660 | " <td>0.0</td>\n", |
||
| 4661 | " <td>0.0</td>\n", |
||
| 4662 | " <td>0.0</td>\n", |
||
| 4663 | " <td>...</td>\n", |
||
| 4664 | " <td>-1.000000</td>\n", |
||
| 4665 | " <td>-1.000000</td>\n", |
||
| 4666 | " <td>-1.000000</td>\n", |
||
| 4667 | " <td>-1.000000</td>\n", |
||
| 4668 | " <td>-1.000000</td>\n", |
||
| 4669 | " <td>-1.000000</td>\n", |
||
| 4670 | " <td>-1.000000</td>\n", |
||
| 4671 | " <td>0</td>\n", |
||
| 4672 | " <td>1</td>\n", |
||
| 4673 | " <td>1</td>\n", |
||
| 4674 | " </tr>\n", |
||
| 4675 | " <tr>\n", |
||
| 4676 | " <th>6</th>\n", |
||
| 4677 | " <td>1</td>\n", |
||
| 4678 | " <td>1</td>\n", |
||
| 4679 | " <td>8</td>\n", |
||
| 4680 | " <td>1</td>\n", |
||
| 4681 | " <td>0.0</td>\n", |
||
| 4682 | " <td>0.0</td>\n", |
||
| 4683 | " <td>0.0</td>\n", |
||
| 4684 | " <td>0.0</td>\n", |
||
| 4685 | " <td>0.0</td>\n", |
||
| 4686 | " <td>0.0</td>\n", |
||
| 4687 | " <td>...</td>\n", |
||
| 4688 | " <td>-1.000000</td>\n", |
||
| 4689 | " <td>-1.000000</td>\n", |
||
| 4690 | " <td>-1.000000</td>\n", |
||
| 4691 | " <td>-1.000000</td>\n", |
||
| 4692 | " <td>-1.000000</td>\n", |
||
| 4693 | " <td>-1.000000</td>\n", |
||
| 4694 | " <td>-1.000000</td>\n", |
||
| 4695 | " <td>1</td>\n", |
||
| 4696 | " <td>1</td>\n", |
||
| 4697 | " <td>1</td>\n", |
||
| 4698 | " </tr>\n", |
||
| 4699 | " <tr>\n", |
||
| 4700 | " <th>7</th>\n", |
||
| 4701 | " <td>1</td>\n", |
||
| 4702 | " <td>1</td>\n", |
||
| 4703 | " <td>8</td>\n", |
||
| 4704 | " <td>1</td>\n", |
||
| 4705 | " <td>0.0</td>\n", |
||
| 4706 | " <td>0.0</td>\n", |
||
| 4707 | " <td>0.0</td>\n", |
||
| 4708 | " <td>0.0</td>\n", |
||
| 4709 | " <td>0.0</td>\n", |
||
| 4710 | " <td>0.0</td>\n", |
||
| 4711 | " <td>...</td>\n", |
||
| 4712 | " <td>-1.000000</td>\n", |
||
| 4713 | " <td>-1.000000</td>\n", |
||
| 4714 | " <td>-1.000000</td>\n", |
||
| 4715 | " <td>-1.000000</td>\n", |
||
| 4716 | " <td>-1.000000</td>\n", |
||
| 4717 | " <td>-1.000000</td>\n", |
||
| 4718 | " <td>-1.000000</td>\n", |
||
| 4719 | " <td>2</td>\n", |
||
| 4720 | " <td>1</td>\n", |
||
| 4721 | " <td>1</td>\n", |
||
| 4722 | " </tr>\n", |
||
| 4723 | " <tr>\n", |
||
| 4724 | " <th>8</th>\n", |
||
| 4725 | " <td>1</td>\n", |
||
| 4726 | " <td>1</td>\n", |
||
| 4727 | " <td>8</td>\n", |
||
| 4728 | " <td>1</td>\n", |
||
| 4729 | " <td>0.0</td>\n", |
||
| 4730 | " <td>0.0</td>\n", |
||
| 4731 | " <td>0.0</td>\n", |
||
| 4732 | " <td>0.0</td>\n", |
||
| 4733 | " <td>0.0</td>\n", |
||
| 4734 | " <td>0.0</td>\n", |
||
| 4735 | " <td>...</td>\n", |
||
| 4736 | " <td>-1.000000</td>\n", |
||
| 4737 | " <td>-0.906832</td>\n", |
||
| 4738 | " <td>-0.831132</td>\n", |
||
| 4739 | " <td>-0.940967</td>\n", |
||
| 4740 | " <td>-0.817204</td>\n", |
||
| 4741 | " <td>-0.882574</td>\n", |
||
| 4742 | " <td>-1.000000</td>\n", |
||
| 4743 | " <td>3</td>\n", |
||
| 4744 | " <td>1</td>\n", |
||
| 4745 | " <td>1</td>\n", |
||
| 4746 | " </tr>\n", |
||
| 4747 | " <tr>\n", |
||
| 4748 | " <th>10</th>\n", |
||
| 4749 | " <td>2</td>\n", |
||
| 4750 | " <td>0</td>\n", |
||
| 4751 | " <td>0</td>\n", |
||
| 4752 | " <td>0</td>\n", |
||
| 4753 | " <td>0.0</td>\n", |
||
| 4754 | " <td>0.0</td>\n", |
||
| 4755 | " <td>0.0</td>\n", |
||
| 4756 | " <td>0.0</td>\n", |
||
| 4757 | " <td>0.0</td>\n", |
||
| 4758 | " <td>0.0</td>\n", |
||
| 4759 | " <td>...</td>\n", |
||
| 4760 | " <td>-0.959596</td>\n", |
||
| 4761 | " <td>-0.515528</td>\n", |
||
| 4762 | " <td>-0.351328</td>\n", |
||
| 4763 | " <td>-0.747001</td>\n", |
||
| 4764 | " <td>-0.756272</td>\n", |
||
| 4765 | " <td>-1.000000</td>\n", |
||
| 4766 | " <td>-0.961262</td>\n", |
||
| 4767 | " <td>0</td>\n", |
||
| 4768 | " <td>0</td>\n", |
||
| 4769 | " <td>1</td>\n", |
||
| 4770 | " </tr>\n", |
||
| 4771 | " <tr>\n", |
||
| 4772 | " <th>11</th>\n", |
||
| 4773 | " <td>2</td>\n", |
||
| 4774 | " <td>0</td>\n", |
||
| 4775 | " <td>0</td>\n", |
||
| 4776 | " <td>0</td>\n", |
||
| 4777 | " <td>0.0</td>\n", |
||
| 4778 | " <td>0.0</td>\n", |
||
| 4779 | " <td>0.0</td>\n", |
||
| 4780 | " <td>0.0</td>\n", |
||
| 4781 | " <td>0.0</td>\n", |
||
| 4782 | " <td>0.0</td>\n", |
||
| 4783 | " <td>...</td>\n", |
||
| 4784 | " <td>-0.959596</td>\n", |
||
| 4785 | " <td>-0.515528</td>\n", |
||
| 4786 | " <td>-0.351328</td>\n", |
||
| 4787 | " <td>-0.747001</td>\n", |
||
| 4788 | " <td>-0.756272</td>\n", |
||
| 4789 | " <td>-1.000000</td>\n", |
||
| 4790 | " <td>-0.961262</td>\n", |
||
| 4791 | " <td>1</td>\n", |
||
| 4792 | " <td>0</td>\n", |
||
| 4793 | " <td>1</td>\n", |
||
| 4794 | " </tr>\n", |
||
| 4795 | " <tr>\n", |
||
| 4796 | " <th>12</th>\n", |
||
| 4797 | " <td>2</td>\n", |
||
| 4798 | " <td>0</td>\n", |
||
| 4799 | " <td>0</td>\n", |
||
| 4800 | " <td>0</td>\n", |
||
| 4801 | " <td>0.0</td>\n", |
||
| 4802 | " <td>0.0</td>\n", |
||
| 4803 | " <td>0.0</td>\n", |
||
| 4804 | " <td>0.0</td>\n", |
||
| 4805 | " <td>0.0</td>\n", |
||
| 4806 | " <td>0.0</td>\n", |
||
| 4807 | " <td>...</td>\n", |
||
| 4808 | " <td>-0.959596</td>\n", |
||
| 4809 | " <td>-0.515528</td>\n", |
||
| 4810 | " <td>-0.351328</td>\n", |
||
| 4811 | " <td>-0.747001</td>\n", |
||
| 4812 | " <td>-0.756272</td>\n", |
||
| 4813 | " <td>-1.000000</td>\n", |
||
| 4814 | " <td>-0.961262</td>\n", |
||
| 4815 | " <td>2</td>\n", |
||
| 4816 | " <td>0</td>\n", |
||
| 4817 | " <td>1</td>\n", |
||
| 4818 | " </tr>\n", |
||
| 4819 | " <tr>\n", |
||
| 4820 | " <th>13</th>\n", |
||
| 4821 | " <td>2</td>\n", |
||
| 4822 | " <td>0</td>\n", |
||
| 4823 | " <td>0</td>\n", |
||
| 4824 | " <td>0</td>\n", |
||
| 4825 | " <td>0.0</td>\n", |
||
| 4826 | " <td>0.0</td>\n", |
||
| 4827 | " <td>0.0</td>\n", |
||
| 4828 | " <td>0.0</td>\n", |
||
| 4829 | " <td>0.0</td>\n", |
||
| 4830 | " <td>0.0</td>\n", |
||
| 4831 | " <td>...</td>\n", |
||
| 4832 | " <td>-0.797980</td>\n", |
||
| 4833 | " <td>-0.658863</td>\n", |
||
| 4834 | " <td>-0.563758</td>\n", |
||
| 4835 | " <td>-0.721834</td>\n", |
||
| 4836 | " <td>-0.926882</td>\n", |
||
| 4837 | " <td>-1.000000</td>\n", |
||
| 4838 | " <td>-0.801293</td>\n", |
||
| 4839 | " <td>3</td>\n", |
||
| 4840 | " <td>0</td>\n", |
||
| 4841 | " <td>1</td>\n", |
||
| 4842 | " </tr>\n", |
||
| 4843 | " <tr>\n", |
||
| 4844 | " <th>15</th>\n", |
||
| 4845 | " <td>3</td>\n", |
||
| 4846 | " <td>0</td>\n", |
||
| 4847 | " <td>3</td>\n", |
||
| 4848 | " <td>1</td>\n", |
||
| 4849 | " <td>0.0</td>\n", |
||
| 4850 | " <td>0.0</td>\n", |
||
| 4851 | " <td>0.0</td>\n", |
||
| 4852 | " <td>0.0</td>\n", |
||
| 4853 | " <td>0.0</td>\n", |
||
| 4854 | " <td>0.0</td>\n", |
||
| 4855 | " <td>...</td>\n", |
||
| 4856 | " <td>-1.000000</td>\n", |
||
| 4857 | " <td>-1.000000</td>\n", |
||
| 4858 | " <td>-1.000000</td>\n", |
||
| 4859 | " <td>-1.000000</td>\n", |
||
| 4860 | " <td>-1.000000</td>\n", |
||
| 4861 | " <td>-1.000000</td>\n", |
||
| 4862 | " <td>-1.000000</td>\n", |
||
| 4863 | " <td>0</td>\n", |
||
| 4864 | " <td>0</td>\n", |
||
| 4865 | " <td>0</td>\n", |
||
| 4866 | " </tr>\n", |
||
| 4867 | " <tr>\n", |
||
| 4868 | " <th>16</th>\n", |
||
| 4869 | " <td>3</td>\n", |
||
| 4870 | " <td>0</td>\n", |
||
| 4871 | " <td>3</td>\n", |
||
| 4872 | " <td>1</td>\n", |
||
| 4873 | " <td>0.0</td>\n", |
||
| 4874 | " <td>0.0</td>\n", |
||
| 4875 | " <td>0.0</td>\n", |
||
| 4876 | " <td>0.0</td>\n", |
||
| 4877 | " <td>0.0</td>\n", |
||
| 4878 | " <td>0.0</td>\n", |
||
| 4879 | " <td>...</td>\n", |
||
| 4880 | " <td>-1.000000</td>\n", |
||
| 4881 | " <td>-1.000000</td>\n", |
||
| 4882 | " <td>-1.000000</td>\n", |
||
| 4883 | " <td>-1.000000</td>\n", |
||
| 4884 | " <td>-1.000000</td>\n", |
||
| 4885 | " <td>-1.000000</td>\n", |
||
| 4886 | " <td>-1.000000</td>\n", |
||
| 4887 | " <td>1</td>\n", |
||
| 4888 | " <td>0</td>\n", |
||
| 4889 | " <td>0</td>\n", |
||
| 4890 | " </tr>\n", |
||
| 4891 | " <tr>\n", |
||
| 4892 | " <th>17</th>\n", |
||
| 4893 | " <td>3</td>\n", |
||
| 4894 | " <td>0</td>\n", |
||
| 4895 | " <td>3</td>\n", |
||
| 4896 | " <td>1</td>\n", |
||
| 4897 | " <td>0.0</td>\n", |
||
| 4898 | " <td>0.0</td>\n", |
||
| 4899 | " <td>0.0</td>\n", |
||
| 4900 | " <td>0.0</td>\n", |
||
| 4901 | " <td>0.0</td>\n", |
||
| 4902 | " <td>0.0</td>\n", |
||
| 4903 | " <td>...</td>\n", |
||
| 4904 | " <td>-1.000000</td>\n", |
||
| 4905 | " <td>-1.000000</td>\n", |
||
| 4906 | " <td>-1.000000</td>\n", |
||
| 4907 | " <td>-1.000000</td>\n", |
||
| 4908 | " <td>-1.000000</td>\n", |
||
| 4909 | " <td>-1.000000</td>\n", |
||
| 4910 | " <td>-1.000000</td>\n", |
||
| 4911 | " <td>2</td>\n", |
||
| 4912 | " <td>0</td>\n", |
||
| 4913 | " <td>0</td>\n", |
||
| 4914 | " </tr>\n", |
||
| 4915 | " </tbody>\n", |
||
| 4916 | "</table>\n", |
||
| 4917 | "<p>15 rows × 232 columns</p>\n", |
||
| 4918 | "</div>\n", |
||
| 4919 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67b9d01c-dc78-4361-a489-e6e610b87730')\"\n", |
||
| 4920 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 4921 | " style=\"display:none;\">\n", |
||
| 4922 | " \n", |
||
| 4923 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 4924 | " width=\"24px\">\n", |
||
| 4925 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 4926 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 4927 | " </svg>\n", |
||
| 4928 | " </button>\n", |
||
| 4929 | " \n", |
||
| 4930 | " <style>\n", |
||
| 4931 | " .colab-df-container {\n", |
||
| 4932 | " display:flex;\n", |
||
| 4933 | " flex-wrap:wrap;\n", |
||
| 4934 | " gap: 12px;\n", |
||
| 4935 | " }\n", |
||
| 4936 | "\n", |
||
| 4937 | " .colab-df-convert {\n", |
||
| 4938 | " background-color: #E8F0FE;\n", |
||
| 4939 | " border: none;\n", |
||
| 4940 | " border-radius: 50%;\n", |
||
| 4941 | " cursor: pointer;\n", |
||
| 4942 | " display: none;\n", |
||
| 4943 | " fill: #1967D2;\n", |
||
| 4944 | " height: 32px;\n", |
||
| 4945 | " padding: 0 0 0 0;\n", |
||
| 4946 | " width: 32px;\n", |
||
| 4947 | " }\n", |
||
| 4948 | "\n", |
||
| 4949 | " .colab-df-convert:hover {\n", |
||
| 4950 | " background-color: #E2EBFA;\n", |
||
| 4951 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 4952 | " fill: #174EA6;\n", |
||
| 4953 | " }\n", |
||
| 4954 | "\n", |
||
| 4955 | " [theme=dark] .colab-df-convert {\n", |
||
| 4956 | " background-color: #3B4455;\n", |
||
| 4957 | " fill: #D2E3FC;\n", |
||
| 4958 | " }\n", |
||
| 4959 | "\n", |
||
| 4960 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 4961 | " background-color: #434B5C;\n", |
||
| 4962 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 4963 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 4964 | " fill: #FFFFFF;\n", |
||
| 4965 | " }\n", |
||
| 4966 | " </style>\n", |
||
| 4967 | "\n", |
||
| 4968 | " <script>\n", |
||
| 4969 | " const buttonEl =\n", |
||
| 4970 | " document.querySelector('#df-67b9d01c-dc78-4361-a489-e6e610b87730 button.colab-df-convert');\n", |
||
| 4971 | " buttonEl.style.display =\n", |
||
| 4972 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 4973 | "\n", |
||
| 4974 | " async function convertToInteractive(key) {\n", |
||
| 4975 | " const element = document.querySelector('#df-67b9d01c-dc78-4361-a489-e6e610b87730');\n", |
||
| 4976 | " const dataTable =\n", |
||
| 4977 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 4978 | " [key], {});\n", |
||
| 4979 | " if (!dataTable) return;\n", |
||
| 4980 | "\n", |
||
| 4981 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 4982 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 4983 | " + ' to learn more about interactive tables.';\n", |
||
| 4984 | " element.innerHTML = '';\n", |
||
| 4985 | " dataTable['output_type'] = 'display_data';\n", |
||
| 4986 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 4987 | " const docLink = document.createElement('div');\n", |
||
| 4988 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 4989 | " element.appendChild(docLink);\n", |
||
| 4990 | " }\n", |
||
| 4991 | " </script>\n", |
||
| 4992 | " </div>\n", |
||
| 4993 | " </div>\n", |
||
| 4994 | " " |
||
| 4995 | ] |
||
| 4996 | }, |
||
| 4997 | "metadata": {}, |
||
| 4998 | "execution_count": 34 |
||
| 4999 | } |
||
| 5000 | ] |
||
| 5001 | }, |
||
| 5002 | { |
||
| 5003 | "cell_type": "code", |
||
| 5004 | "source": [ |
||
| 5005 | "score_remove_window_4 = log_reg_model(model_data)" |
||
| 5006 | ], |
||
| 5007 | "metadata": { |
||
| 5008 | "id": "5gIvdnRiE8q4", |
||
| 5009 | "colab": { |
||
| 5010 | "base_uri": "https://localhost:8080/", |
||
| 5011 | "height": 728 |
||
| 5012 | }, |
||
| 5013 | "outputId": "92caa60c-2145-4ef3-eaa4-3d56377ed60f" |
||
| 5014 | }, |
||
| 5015 | "execution_count": 35, |
||
| 5016 | "outputs": [ |
||
| 5017 | { |
||
| 5018 | "output_type": "stream", |
||
| 5019 | "name": "stdout", |
||
| 5020 | "text": [ |
||
| 5021 | "Training data: (1347, 231)\n", |
||
| 5022 | "Test data: (578, 231)\n", |
||
| 5023 | "\n" |
||
| 5024 | ] |
||
| 5025 | }, |
||
| 5026 | { |
||
| 5027 | "output_type": "stream", |
||
| 5028 | "name": "stderr", |
||
| 5029 | "text": [ |
||
| 5030 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", |
||
| 5031 | " y = column_or_1d(y, warn=True)\n" |
||
| 5032 | ] |
||
| 5033 | }, |
||
| 5034 | { |
||
| 5035 | "output_type": "stream", |
||
| 5036 | "name": "stdout", |
||
| 5037 | "text": [ |
||
| 5038 | " precision recall f1-score support\n", |
||
| 5039 | "\n", |
||
| 5040 | " 0 0.94 0.95 0.95 423\n", |
||
| 5041 | " 1 0.87 0.83 0.85 155\n", |
||
| 5042 | "\n", |
||
| 5043 | " accuracy 0.92 578\n", |
||
| 5044 | " macro avg 0.90 0.89 0.90 578\n", |
||
| 5045 | "weighted avg 0.92 0.92 0.92 578\n", |
||
| 5046 | "\n", |
||
| 5047 | "ROC_AUC_Score: 0.8924883703195303\n" |
||
| 5048 | ] |
||
| 5049 | }, |
||
| 5050 | { |
||
| 5051 | "output_type": "display_data", |
||
| 5052 | "data": { |
||
| 5053 | "text/plain": [ |
||
| 5054 | "<Figure size 700x500 with 2 Axes>" |
||
| 5055 | ], |
||
| 5056 | "image/png": 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\n" |
||
| 5057 | }, |
||
| 5058 | "metadata": {} |
||
| 5059 | } |
||
| 5060 | ] |
||
| 5061 | }, |
||
| 5062 | { |
||
| 5063 | "cell_type": "code", |
||
| 5064 | "source": [ |
||
| 5065 | "score_remove_window_4 = decision_tree_model(model_data)" |
||
| 5066 | ], |
||
| 5067 | "metadata": { |
||
| 5068 | "id": "y-0C9W85FCef", |
||
| 5069 | "colab": { |
||
| 5070 | "base_uri": "https://localhost:8080/", |
||
| 5071 | "height": 671 |
||
| 5072 | }, |
||
| 5073 | "outputId": "cc3db6b1-2ce9-4949-9717-d137aebab8a5" |
||
| 5074 | }, |
||
| 5075 | "execution_count": 36, |
||
| 5076 | "outputs": [ |
||
| 5077 | { |
||
| 5078 | "output_type": "stream", |
||
| 5079 | "name": "stdout", |
||
| 5080 | "text": [ |
||
| 5081 | "Training data: (1347, 231)\n", |
||
| 5082 | "Test data: (578, 231)\n", |
||
| 5083 | "\n", |
||
| 5084 | " precision recall f1-score support\n", |
||
| 5085 | "\n", |
||
| 5086 | " 0 0.94 0.94 0.94 423\n", |
||
| 5087 | " 1 0.84 0.83 0.83 155\n", |
||
| 5088 | "\n", |
||
| 5089 | " accuracy 0.91 578\n", |
||
| 5090 | " macro avg 0.89 0.88 0.89 578\n", |
||
| 5091 | "weighted avg 0.91 0.91 0.91 578\n", |
||
| 5092 | "\n", |
||
| 5093 | "ROC_AUC_Score: 0.8845344314802104\n" |
||
| 5094 | ] |
||
| 5095 | }, |
||
| 5096 | { |
||
| 5097 | "output_type": "display_data", |
||
| 5098 | "data": { |
||
| 5099 | "text/plain": [ |
||
| 5100 | "<Figure size 700x500 with 2 Axes>" |
||
| 5101 | ], |
||
| 5102 | "image/png": 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94Q9/0N69e9WxY0fNmDFD7u7uZZ77S8tSPPzww6zEiypRr149TZ06VU2aNJHNZtOCBQvUu3dv7d69W82bN9eYMWP0xRdfaOnSpfL399fo0aPVt29fbdmyRVLp3K6ePXsqJCREW7duVXp6uoYMGSJPT09NmTLFqbE49VECbm5u113y32KxqKioyKlBXLkOFe+BBx6Qr6+v1q5d67AAoYeHh0aNGqV33nlHVqtVYWFhOn36tCSpfv36ys/Pt5etpdIqysyZMzVixAjNmzfvqpV4pdKQu2vXLod9jz76qBYvXqxLly4pPDzcvo4MKh4LTlat4uJiPffcc0pKSlLbtm315z//WT4+Pjd0LwIM+jX72mX3Wv59l+uf9AsCAwP1xhtv6PHHH1edOnW0ePFiPf7445Kk77//XuHh4UpJSVH79u311Vdf6ZFHHlFaWpq9KjN37ly9+OKLOnv2rFNLBThVgVmxYsU1j6WkpGjmzJk8VXKTa9KkiebPn6+zZ89q165dOn/+vO644w5FRkaqbt26unz5soYNG2YPL1LpD8sPP/xQO3fuVGpqqnx8fNShQwfVrl1bq1evtn9swH/auXOnjh49qkOHDikvL08tWrRQixYtdO7cOcXExBBecFv5+OOP7R96WqtWrWvOAxw/fjyTdnFdbi5sIVmt1qs+XqY8nZLi4mItXbpUeXl5ioqK0q5du1RYWOiwPEezZs3UoEEDe4BJSUlRZGSkQ0spOjpasbGxOnDgQLmmoVzhVID5z+XiJenw4cP63//9X33++ecaNGiQJk+e7MwtUcmSk5P1+uuv69e//rVatmypO+64QwUFBTpx4oSWLVummTNnXtU+2rVrl5YtW6b27durVatWslqt2rdvn+bNm6d58+Zd87XeeustPfTQQ+rYsaN8fHyUmpqqGTNmaNq0aQ7VHOB28POnLcv69PYrRo8eTYBBpSrrwZpXXnlFkyZNKvP8ffv2KSoqSvn5+apZs6ZWrFihiIgI7dmzR15eXletJh0cHKyMjAxJpZ+H9/PwcuX4lWPOuOE5MGlpaXrllVe0YMECRUdHa8+ePWrRosWN3g6V5MSJE5owYYJT1+zfv18DBw50+rXGjh3r9DXArerZZ5/Vs88+65J7rV+/3iX3gbnc5boKTFkP1vxS9SUsLEx79uxRdna2li1bpqFDhyo5Odll4ykvpwNMdna2pkyZovfee0+tWrXSunXr9OCDD1bE2AAAQBncXBhgnH2wxsvLS3fffbek0rmOO3bs0Lvvvqvf/e539g8k/XkVJjMz0z5XKyQkRNu3b3e4X2Zmpv2YM5xayG769Olq1KiRVq1apU8++URbt24lvAAAcBsrKSmR1WpVmzZt5OnpqXXr1tmPHT58WKmpqYqKipIkRUVFad++fTpz5oz9nKSkJPn5+SkiIsKp13X6KSQfHx916dLlmo/+SdKnn37q1CAknkICKhpPIQG3jicjVrnsXh8ffKTc5yYkJCgmJkYNGjTQxYsXtXjxYk2bNk1r1qxR165dFRsbqy+//FLz58+Xn5+fvW26detWSaUTf1u1aqW6detq+vTpysjI0ODBg/X73//e6ceonWohDRkyhKABAEAVc+UcGGecOXNGQ4YMUXp6uvz9/dWyZUt7eJGkt99+W25uburXr5/DQnb2cbu7a9WqVYqNjVVUVJRq1KihoUOH3tADQE5VYCoSwQioWDfJtzoAFxga8XeX3WvBwUdddq/KdMNPIQEAgKrhynVgTEWAAQDAMFXVQrqZOPUUEgAAwM2ACgwAAIZx5TowpiLAAABgGHfmwNBCAgAA5qECAwCAYWghEWAAADAOj1HTQgIAAAaiAgMAgGFYB4YAAwCAcZgDQwsJAAAYiAoMAACGYR0YAgwAAMahhUQLCQAAGIgKDAAAhmEdGAIMAADG4TFqWkgAAMBAVGAAADAMk3gJMAAAGIfHqGkhAQAAA1GBAQDAMLSQCDAAABiHx6hpIQEAAANRgQEAwDCsA0OAAQDAOMyBoYUEAAAMRAUGAADDMImXAAMAgHGYA0MLCQAAGIgKDAAAhqGFRIABAMA4tJBoIQEAAANRgQEAwDCsA0OAAQDAOO7MgaGFBAAAzEMFBgAAw7ipuKqHUOUIMAAAGIYWEi0kAABgICowAAAYxoMWEhUYAABM465il23OSExM1H333SdfX18FBQWpT58+Onz4sMM5Dz30kCwWi8M2atQoh3NSU1PVs2dPVa9eXUFBQXrhhRdUVFTk1FiowAAAgHJJTk5WXFyc7rvvPhUVFemll15St27ddPDgQdWoUcN+3ogRIzR58mT736tXr27/c3FxsXr27KmQkBBt3bpV6enpGjJkiDw9PTVlypRyj4UAAwCAYdwtVdNCWr16tcPf58+fr6CgIO3atUudOnWy769evbpCQkLKvMfatWt18OBBff311woODlarVq302muv6cUXX9SkSZPk5eVVrrHQQgIAwDCubCFZrVbl5OQ4bFartVzjyM7OliQFBgY67F+0aJHuuOMOtWjRQgkJCbp06ZL9WEpKiiIjIxUcHGzfFx0drZycHB04cKDc7wEBBgCA21hiYqL8/f0dtsTExOteV1JSoueff14dOnRQixYt7PsHDhyojz/+WBs2bFBCQoI++ugjPfnkk/bjGRkZDuFFkv3vGRkZ5R43LSQAAAzjynVgEhISFB8f77DP29v7utfFxcVp//792rx5s8P+kSNH2v8cGRmp0NBQde7cWceOHVPjxo1dM2gRYAAAMI4rH6P29vYuV2D5udGjR2vVqlXatGmT6tWr94vntmvXTpJ09OhRNW7cWCEhIdq+fbvDOZmZmZJ0zXkzZaGFBAAAysVms2n06NFasWKF1q9fr7vuuuu61+zZs0eSFBoaKkmKiorSvn37dObMGfs5SUlJ8vPzU0RERLnHQgUGAADDOLt+i6vExcVp8eLF+uyzz+Tr62ufs+Lv7y8fHx8dO3ZMixcvVo8ePVS7dm3t3btXY8aMUadOndSyZUtJUrdu3RQREaHBgwdr+vTpysjI0IQJExQXF+dUJchis9lsFfJVOslisVT1EIBb2k3yrQ7ABVa3H3n9k8qp+7d/Kve51/pdPW/ePA0bNkynTp3Sk08+qf379ysvL0/169fXY489pgkTJsjPz89+/smTJxUbG6uNGzeqRo0aGjp0qKZOnSoPj/LXVQgwwG3iJvlWB+ACVRVgbia0kAAAMExVtZBuJgQYAAAM48rHqE3FU0gAAMA4VGAAADCMK9eBMRUBBgAAwzAHhhYSAAAwEBUYAAAM426hAkOAAQDAMLSQaCEBAAADUYEBAMAwrANDgAEAwDg8Rk0LCQAAGIgKDAAAhmESLwEGAADj8Bg1LSQAAGAgKjAAABiGFhIBBgAA4/AYNS0kAABgICowAAAYhnVgCDAAABiHOTC0kAAAgIGowAAAYBjWgSHAAABgHFpItJAAAICBqMAAAGAY1oEhwAAAYBweo6aFBAAADEQFBgAAwzCJlwADAIBxeIyaFhIAADAQFRgAAAxDC4kAAwCAcXiMmhYSAAAw0E1TgSkuvlzVQwBuaemrgqp6CMAtLfSRM5X2Wm5UYG6eAAMAAMrH5ua6AGNx2Z0qFy0kAABgHCowAAAYxuZmc9m9TK3AEGAAADCMKwOMqWghAQAA41CBAQDAMFRgCDAAAJiHAEMLCQAAlE9iYqLuu+8++fr6KigoSH369NHhw4cdzsnPz1dcXJxq166tmjVrql+/fsrMzHQ4JzU1VT179lT16tUVFBSkF154QUVFRU6NhQADAIBhbG4lLtuckZycrLi4OH377bdKSkpSYWGhunXrpry8PPs5Y8aM0eeff66lS5cqOTlZaWlp6tu3r/14cXGxevbsqYKCAm3dulULFizQ/PnzNXHiRKfGYrHZbDdFHaqkJL+qhwDc0jK/bFDVQwBuaZW5Em9BH9d9P3utTL3ha8+ePaugoCAlJyerU6dOys7OVp06dbR48WI9/vjjkqTvv/9e4eHhSklJUfv27fXVV1/pkUceUVpamoKDgyVJc+fO1YsvvqizZ8/Ky8urXK9NBQYAgNuY1WpVTk6Ow2a1Wst1bXZ2tiQpMDBQkrRr1y4VFhaqS5cu9nOaNWumBg0aKCUlRZKUkpKiyMhIe3iRpOjoaOXk5OjAgQPlHjcBBgAAw9jcbC7bEhMT5e/v77AlJiZedwwlJSV6/vnn1aFDB7Vo0UKSlJGRIS8vLwUEBDicGxwcrIyMDPs5Pw8vV45fOVZePIUEAIBhXPkYdUJCguLj4x32eXt7X/e6uLg47d+/X5s3b3bZWJxBgAEA4Dbm7e1drsDyc6NHj9aqVau0adMm1atXz74/JCREBQUFysrKcqjCZGZmKiQkxH7O9u3bHe535SmlK+eUBy0kAABM42Zz3eYEm82m0aNHa8WKFVq/fr3uuusuh+Nt2rSRp6en1q1bZ993+PBhpaamKioqSpIUFRWlffv26cyZf096TkpKkp+fnyIiIso9FiowAAAYxtnHn10lLi5Oixcv1meffSZfX1/7nBV/f3/5+PjI399fw4cPV3x8vAIDA+Xn56dnn31WUVFRat++vSSpW7duioiI0ODBgzV9+nRlZGRowoQJiouLc6oSxGPUwG2Cx6iBilWZj1Ff7h/qsnv5LEkv97kWS9mfXT1v3jwNGzZMUulCdmPHjtUnn3wiq9Wq6OhozZ4926E9dPLkScXGxmrjxo2qUaOGhg4dqqlTp8rDo/x1FQIMcJsgwAAVqzIDzKWB5Z8rcj3VF5f/yZ+bCS0kAAAMw4c5MokXAAAYiAoMAACGoQJDgAEAwDwEGFpIAADAPFRgAAAwTFWtA3MzIcAAAGAY5sDQQgIAAAaiAgMAgGGowBBgAAAwDgGGFhIAADAQFRgAAExDBYYAAwCAaXiMmhYSAAAwEBUYAAAMwyReAgwAAMYhwNBCAgAABqICAwCAYajAEGAAADAPAYYWEgAAMA8VGAAADMM6MAQYAACMwxwYWkgAAMBAVGAAADAMFRgCDAAAxiHA0EICAAAGogIDAIBpqMAQYAAAMA2PUdNCAgAABqICAwCAadwsVT2CKkeAAQDANPRPeAsAAIB5qMAAAGAaWkgEGAAAjEOAoYUEAADMQwUGAADTUIEhwAAAYBwCDC0kAABgHiowAACYhvIDAQYAAOPQQiLDAQCA8tm0aZN69eqlunXrymKxaOXKlQ7Hhw0bJovF4rB1797d4ZwLFy5o0KBB8vPzU0BAgIYPH67c3Fynx0KAAQDANG4W121OyMvL0z333KP333//mud0795d6enp9u2TTz5xOD5o0CAdOHBASUlJWrVqlTZt2qSRI0c6/RbQQgIAwDRV1EKKiYlRTEzML57j7e2tkJCQMo8dOnRIq1ev1o4dO9S2bVtJ0nvvvacePXrozTffVN26dcs9FiowAADcxqxWq3Jychw2q9V6w/fbuHGjgoKCFBYWptjYWJ0/f95+LCUlRQEBAfbwIkldunSRm5ubtm3b5tTrEGAAADCNC1tIiYmJ8vf3d9gSExNvaFjdu3fXwoULtW7dOk2bNk3JycmKiYlRcXGxJCkjI0NBQUEO13h4eCgwMFAZGRlOvRYtJAAATOPC8kNCQoLi4+Md9nl7e9/Qvfr372//c2RkpFq2bKnGjRtr48aN6ty58381zv9EBQYAgNuYt7e3/Pz8HLYbDTD/qVGjRrrjjjt09OhRSVJISIjOnDnjcE5RUZEuXLhwzXkz10KAAQDANFX0FJKzTp8+rfPnzys0NFSSFBUVpaysLO3atct+zvr161VSUqJ27do5dW9aSAAAmKaKnkLKzc21V1Mk6fjx49qzZ48CAwMVGBioV199Vf369VNISIiOHTum8ePH6+6771Z0dLQkKTw8XN27d9eIESM0d+5cFRYWavTo0erfv79TTyBJVGAAAEA57dy5U61bt1br1q0lSfHx8WrdurUmTpwod3d37d27V48++qiaNm2q4cOHq02bNvrmm28cWlKLFi1Ss2bN1LlzZ/Xo0UMdO3bUn/70J6fHYrHZbDaXfWX/hZKS/KoeAnBLy/yyQVUPAbilhT5y5vonuUj6//N12b1Cn77osntVJlpIAACYhs9CooUEAADMQwUGAADTUH4gwAAAYBxaSGQ4AABgHiowAACYhgoMAQYAAOMQYGghAQAA81CBAQDANJQfCDAAABiHFhIZDgAAmIcKDAAApqECQ4ABAMA4BBhaSAAAwDxUYAAAMA0VGCowAADAPFRgAAAwDRUY5wPM5cuXZbPZVL16dUnSyZMntWLFCkVERKhbt24uHyAqx+XLl7VlS4o2bEjWd9/tVlpautzd3dSgQQN17dpZw4YNUY0a1R2uCQ+/57r3bdfuPs2f/+eKGjZwUzp8yl07f/DQ96c8dCjVQ+eyS4vdG2f8dNW5JSXS/hMe2nrAU98d9dCps+4qKpLqBJSoTdMiDfxNvkJrl5T5OgVF0rJN3tqwx0unzrqruFiq7V+iNk2KNKhzvupe4zrcAuifOB9gevfurb59+2rUqFHKyspSu3bt5OnpqXPnzumtt95SbGxsRYwTFWzVqq80ceKrkqTGjRvp4YcfUm5urnbv/odmzZqjL79crYUL/6LatWvbr+nT59Fr3i85+Rv99NNPatPm3gofO3CzWZhUTVsOeJXr3LQLbvqf930lSYG+Jbr37kK5uUmHUj30eYq31n3npam/v6iWjYodrrMWSmPm+OrgSQ/V9ClRq8ZF8vKw6Ycf3fXFttJQ83bsRYXVLy7rZQHjOR1gvvvuO7399tuSpGXLlik4OFi7d+/W8uXLNXHiRAKMoTw9PfTEE/00ZMiTaty4kX3/mTNnNWrUaB069L0SE9/Qm29OtR9LTHytzHvl5OToyy9XS5J69epZsQMHbkLN7yxS47rFala/WGH1i9T/dX8VFpVd8rdIatu0UAMfzlfru4tk+ddpBUXSW8uqa/UOb72+uIYWJeTIw/3f16361lsHT3qoWf0ivfnMRdX0Kd1fXCLNWumjFVuqafbfffRuXG7FfrGoGrSQnA8wly5dkq9v6b8W1q5dq759+8rNzU3t27fXyZMnXT5AVI4+fR4ts6ISFFRHL7/8kgYOHKKkpHUqKCiUl5fnL95r9eokFRQU6J57WurOOxtW1JCBm9bAh63lPvdXd5TozWeuDhleHtKYfpf0zT5PZf7krv0nPNSqcZH9+D/+Wfrj+7e/zreHF0lyd5Oe7p6vFVuq6ftTTHO8ZRFgnO+i3X333Vq5cqVOnTqlNWvW2Oe9nDlzRn5+fi4fIKpes2ZNJUkFBQXKysq67vmff/6FJOnRRx+pyGEBtzxvT6l+ndJ5LOezHX9hebnbrnu9X/XrnwOYyukAM3HiRI0bN0533nmn7r//fkVFRUkqrca0bt3a5QNE1Tt16rSk0jZTQID/L56blpauXbu+k6enh2JioitjeMAtq6REyvyp9Md0oJ9jGGkbVlqNWZpcTbmX/72/uET6f6urSZJ6tCt/JQiGcbO4bjOU0/XFxx9/XB07dlR6erruueffT6F07txZjz32mEsHh5vDRx8tliR17NhBXl6/PDFx1aovZbPZ9OCDHVWrVkAljA64da3b7aWfct0UULNEze8scjjWtU2Btn/vqfV7vNT/dX+1uLNIXp7SD6fd9dNFN/V/KF9DuuZX0chR4QwOHq5yQw3SkJAQ5ebmKikpSZ06dZKPj4/uu+8+WSy8obea5ORvtHz5Cnl6euh//ifuuuf//e+rJNE+Av5bZ36yaNZnpZNbnoq+LK//+Gnt7ib936A8Bdcq0ScbqunbQ//+x0XTekW6t0mh3HnUFrcwpwPM+fPn9cQTT2jDhg2yWCw6cuSIGjVqpOHDh6tWrVqaMWPGde9htVpltTqWNj09bfL29nZ2OKhA//zncb344kuy2WwaNy5ezZqF/eL5Bw4c0rFj/5Sfn69+85tfV9IogVvPZav08oKays5zU8cWBer9QMFV51y8ZNGE+TV0+JSHnu1zSZ1aFqiaZ+nk3pkrqut//1xTEwbl6eHWhVXwFaDCEU6dfwvGjBkjT09Ppaam2hezk6Tf/e53Wr16dbnukZiYKH9/f4dt6tQ3nB0KKlBmZqZGjPiDsrNzNGzYYA0ZMui613z+eWn1JTq623VbTQDKVlQsTVpYU4dPeSjyrkK9/GRemefN+sxH/zjmqeExl9XvQavq+NvkW92mji0KNXlYrmySZn9eXUUsA3Nrsri7bjOU0xWYtWvXas2aNapXr57D/iZNmpT7MeqEhATFx8c77PP0ZLb8zSIrK1vDh49SWlqa+vbtrfHjx173muLiYn355RpJrP0C3KiSEinxkxra9r2n7q5bpCnD8+RdxqoFxSXS+t2l/0j4dcurqzPN6hcrNLBEaefdlXbeTQ2CWJEXtx6nA0xeXp5D5eWKCxculLsF5O3tfdW5JSVMNrsZ5OVd0jPP/EHHjv1TXbt21uTJr5RrbtO3327T2bNnVbduXbVty+q7wI2YucJH63Z7qX6dYr0xMle+PmX/wy4r16LC4tLvy5rVyj6nxr/2X7zM3MRbksGVE1cpdwspLS1NkvTggw9q4cKF9v0Wi0UlJSWaPn26fvOb37h+hKg0BQUFGj36Oe3du18dOz6gN9+cJnf38n2T/P3vpWu/9OrVk8ncwA3481fVtHJrNQXXKtabz1xULd9rV6V9q9vk+a91YA6fvvrfoXn50qmzpd+7IbWovtyS3Nxdtxmq3BWY5s2b6/3339cbb7yhhx9+WDt37lRBQYHGjx+vAwcO6MKFC9qyZUtFjhUVqLi4WGPHvqhvv92uNm3u1cyZb113xd0rLl++rK+/Xi9J6t2bp48AZy1N9tbHX/so0LdEM57JVXCtX26pe3lI9zcr1JYDXnr/Mx9NG5Gr2v9aJ8ZaKL29vLryCyxqcWeRfT9wqyl3gHn99df1zDPPqHv37jp48KDmzp0rX19f5ebmqm/fvoqLi1NoaGhFjhUVaNGiJfYQUqtWgCZPnlLmeePHx6tWrVoO+9at26BLly4pMrK57rrrzooeKnDTSznooYVJ/17f/8pE2th3fe37hnS9rKiIIh350V2zPy89NzSwRB99Xa3Me/ZsZ3X4QMc/PHpZB1M9dDTNQ4On+qt5wyJ5edp0+JSHzuW4ya96ieIfL3sCMG4BtJDKH2D+8Ic/KCYmRsOHD1fz5s31pz/9Sf/3f/9XkWNDJcrJybH/+UqQKcvo0aOuCjA/bx8BkLJy3XQo9eofrz/fl5Vb2sHPvWyRzVbadj1w0kMHTpb9Y7lV4yKHAPOrO0r0l7E5+mR9NW373lP/+KeHbJKCAkrUp0O+Bj6cr6AAqi+3LAufc2Wx2WxO/x8+a9YsjRkzRuHh4fLwcHwTv/vuuxsaCJN4gYqV+WWDqh4CcEsLfeRMpb1W+tYIl90r9IGDLrtXZXI6wp08eVKffvqpatWqpd69e18VYAAAQAWjheRcgPnwww81duxYdenSRQcOHFCdOnUqalwAAOBaCDDlDzDdu3fX9u3bNWvWLA0ZMqQixwQAAPCLyh1giouLtXfv3qtW4AUAAJXM4PVbXKXcASYpKakixwEAAMqLFhKfZwkAAMxDgAEAwDQWD9dtTti0aZN69eqlunXrymKxaOXKlQ7HbTabJk6cqNDQUPn4+KhLly46cuSIwzkXLlzQoEGD5Ofnp4CAAA0fPly5ublOvwUEGAAATGNxd93mhLy8PN1zzz16//33yzw+ffp0zZw5U3PnztW2bdtUo0YNRUdHKz//32u9DRo0SAcOHFBSUpJWrVqlTZs2aeTIkc6/BTeykF1FYCE7oGKxkB1QsSp1IbtdD7rsXqFtvrmh6ywWi1asWKE+ffpIKq2+1K1bV2PHjtW4ceMkSdnZ2QoODtb8+fPVv39/HTp0SBEREdqxY4fatm0rSVq9erV69Oih06dPq27duuV+fSowAACYxoUVGKvVqpycHIfNarU6PaTjx48rIyNDXbp0se/z9/dXu3btlJKSIklKSUlRQECAPbxIUpcuXeTm5qZt27Y59XoEGAAATOPm7rItMTFR/v7+DltiYqLTQ8rIyJAkBQcHO+wPDg62H8vIyFBQUJDDcQ8PDwUGBtrPKS8+BwAAgNtYQkKC4uPjHfZ5e3tX0WjKjwADAIBpXPhp1N7e3i4JLCEhIZKkzMxMhYaG2vdnZmaqVatW9nPOnHGcK1RUVKQLFy7Yry8vWkgAAJimip5C+iV33XWXQkJCtG7dOvu+nJwcbdu2TVFRUZKkqKgoZWVladeuXfZz1q9fr5KSErVr186p16MCAwAAyiU3N1dHjx61//348ePas2ePAgMD1aBBAz3//PP64x//qCZNmuiuu+7Syy+/rLp169qfVAoPD1f37t01YsQIzZ07V4WFhRo9erT69+/v1BNIEgEGAADzVNFHCezcuVO/+c1v7H+/Mndm6NChmj9/vsaPH6+8vDyNHDlSWVlZ6tixo1avXq1q1arZr1m0aJFGjx6tzp07y83NTf369dPMmTOdHgvrwAC3CdaBASpWpa4Ds7+Py+4V2mKly+5VmZgDAwAAjEMLCQAA07jxadQEGAAATOPCx6hNRQsJAAAYhwgHAIBpqugppJsJAQYAANMQYGghAQAA81CBAQDANFRgCDAAABiHx6hpIQEAAPNQgQEAwDSsA0OAAQDAOMyBoYUEAADMQwUGAADTUIEhwAAAYBwCDC0kAABgHiowAACYhnVgCDAAABiHx6hpIQEAAPMQ4QAAMA2TeAkwAAAYhwBDCwkAAJiHCgwAAKahAkOAAQDAODyFRAsJAACYhwgHAIBhLFRgCDAAAJiGAEMLCQAAGIgIBwCAYajAEGAAADAQv75pIQEAAOMQ4QAAMAwtJAIMAADGIcDQQgIAAAYiwgEAYBgqMAQYAACMQ4ChhQQAAAxEhAMAwDj8+uYdAADAMLSQaCEBAAADEeEAADAMFRgqMAAAGMdi8XDZ5oxJkybJYrE4bM2aNbMfz8/PV1xcnGrXrq2aNWuqX79+yszMdPWXL4kAAwAAnNC8eXOlp6fbt82bN9uPjRkzRp9//rmWLl2q5ORkpaWlqW/fvhUyDmpQAAAYpipbSB4eHgoJCblqf3Z2tv7yl79o8eLFevjhhyVJ8+bNU3h4uL799lu1b9/epeOgAgMAgHE8XLZZrVbl5OQ4bFar9ZqvfOTIEdWtW1eNGjXSoEGDlJqaKknatWuXCgsL1aVLF/u5zZo1U4MGDZSSkuLir58AAwDAbS0xMVH+/v4OW2JiYpnntmvXTvPnz9fq1as1Z84cHT9+XA8++KAuXryojIwMeXl5KSAgwOGa4OBgZWRkuHzctJAAADCMK1tICQkJio+Pd9jn7e1d5rkxMTH2P7ds2VLt2rVTw4YN9be//U0+Pj4uG1N5EGAAADCMKwOMt7f3NQPL9QQEBKhp06Y6evSounbtqoKCAmVlZTlUYTIzM8ucM/PfooUEAABuSG5uro4dO6bQ0FC1adNGnp6eWrdunf344cOHlZqaqqioKJe/NhUYAAAMU1VPIY0bN069evVSw4YNlZaWpldeeUXu7u4aMGCA/P39NXz4cMXHxyswMFB+fn569tlnFRUV5fInkCQCDAAAxqmqAHP69GkNGDBA58+fV506ddSxY0d9++23qlOnjiTp7bfflpubm/r16yer1aro6GjNnj27QsZisdlstgq5s5NKSvKregjALS3zywZVPQTglhb6yJlKe61Ll3a67F7Vq7d12b0qExUYAACMw69v3gEAAAzDhznyFBIAADAQEQ4AAMNQgSHAAABgHAIMLSQAAGAgIhwAAIahAkOAAQDAQPz6poUEAACMQ4QDAMAwtJAIMAAAGIcAQwsJAAAYiAgHAIBhqMAQYAAAMA4BhhYSAAAwEBEOAADj8OubdwAAAMPQQqKFBAAADESEAwDAMFRgJIvNZrNV9SBgFqvVqsTERCUkJMjb27uqhwPccvgeA66PAAOn5eTkyN/fX9nZ2fLz86vq4QC3HL7HgOtjDgwAADAOAQYAABiHAAMAAIxDgIHTvL299corrzC5EKggfI8B18ckXgAAYBwqMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAQZmGDRsmi8WiqVOnOuxfuXKlLBZLFY0KMJvNZlOXLl0UHR191bHZs2crICBAp0+froKRAeYhwOCaqlWrpmnTpumnn36q6qEAtwSLxaJ58+Zp27Zt+uCDD+z7jx8/rvHjx+u9995TvXr1qnCEgDkIMLimLl26KCQkRImJidc8Z/ny5WrevLm8vb115513asaMGZU4QsA89evX17vvvqtx48bp+PHjstlsGj58uLp166bWrVsrJiZGNWvWVHBwsAYPHqxz587Zr122bJkiIyPl4+Oj2rVrq0uXLsrLy6vCrwaoOgQYXJO7u7umTJmi9957r8yy9q5du/TEE0+of//+2rdvnyZNmqSXX35Z8+fPr/zBAgYZOnSoOnfurKefflqzZs3S/v379cEHH+jhhx9W69attXPnTq1evVqZmZl64oknJEnp6ekaMGCAnn76aR06dEgbN25U3759xVqkuF2xEi/KNGzYMGVlZWnlypWKiopSRESE/vKXv2jlypV67LHHZLPZNGjQIJ09e1Zr1661Xzd+/Hh98cUXOnDgQBWOHrj5nTlzRs2bN9eFCxe0fPly7d+/X998843WrFljP+f06dOqX7++Dh8+rNzcXLVp00YnTpxQw4YNq3DkwM2BCgyua9q0aVqwYIEOHTrksP/QoUPq0KGDw74OHTroyJEjKi4urswhAsYJCgrSM888o/DwcPXp00f/+Mc/tGHDBtWsWdO+NWvWTJJ07Ngx3XPPPercubMiIyP129/+Vh9++CHz03BbI8Dgujp16qTo6GglJCRU9VCAW4qHh4c8PDwkSbm5uerVq5f27NnjsB05ckSdOnWSu7u7kpKS9NVXXykiIkLvvfeewsLCdPz48Sr+KoCq4VHVA4AZpk6dqlatWiksLMy+Lzw8XFu2bHE4b8uWLWratKnc3d0re4iA0e69914tX75cd955pz3U/CeLxaIOHTqoQ4cOmjhxoho2bKgVK1YoPj6+kkcLVD0qMCiXyMhIDRo0SDNnzrTvGzt2rNatW6fXXntNP/zwgxYsWKBZs2Zp3LhxVThSwExxcXG6cOGCBgwYoB07dujYsWNas2aNnnrqKRUXF2vbtm2aMmWKdu7cqdTUVH366ac6e/aswsPDq3roQJUgwKDcJk+erJKSEvvf7733Xv3tb3/TkiVL1KJFC02cOFGTJ0/WsGHDqm6QgKHq1q2rLVu2qLi4WN26dVNkZKSef/55BQQEyM3NTX5+ftq0aZN69Oihpk2basKECZoxY4ZiYmKqeuhAleApJAAAYBwqMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwzv8Hj31IGl83wjgAAAAASUVORK5CYII=\n" |
||
| 5103 | }, |
||
| 5104 | "metadata": {} |
||
| 5105 | } |
||
| 5106 | ] |
||
| 5107 | }, |
||
| 5108 | { |
||
| 5109 | "cell_type": "code", |
||
| 5110 | "source": [ |
||
| 5111 | "score_remove_window_4 = k_neighbors_model(model_data)" |
||
| 5112 | ], |
||
| 5113 | "metadata": { |
||
| 5114 | "id": "RddfAhWPFD_e", |
||
| 5115 | "colab": { |
||
| 5116 | "base_uri": "https://localhost:8080/", |
||
| 5117 | "height": 725 |
||
| 5118 | }, |
||
| 5119 | "outputId": "b465a086-543c-456b-8e9b-2c7599c4aa3a" |
||
| 5120 | }, |
||
| 5121 | "execution_count": 37, |
||
| 5122 | "outputs": [ |
||
| 5123 | { |
||
| 5124 | "output_type": "stream", |
||
| 5125 | "name": "stdout", |
||
| 5126 | "text": [ |
||
| 5127 | "Training data: (1347, 231)\n", |
||
| 5128 | "Test data: (578, 231)\n", |
||
| 5129 | "\n" |
||
| 5130 | ] |
||
| 5131 | }, |
||
| 5132 | { |
||
| 5133 | "output_type": "stream", |
||
| 5134 | "name": "stderr", |
||
| 5135 | "text": [ |
||
| 5136 | "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", |
||
| 5137 | " return self._fit(X, y)\n" |
||
| 5138 | ] |
||
| 5139 | }, |
||
| 5140 | { |
||
| 5141 | "output_type": "stream", |
||
| 5142 | "name": "stdout", |
||
| 5143 | "text": [ |
||
| 5144 | " precision recall f1-score support\n", |
||
| 5145 | "\n", |
||
| 5146 | " 0 0.77 1.00 0.87 423\n", |
||
| 5147 | " 1 0.97 0.19 0.31 155\n", |
||
| 5148 | "\n", |
||
| 5149 | " accuracy 0.78 578\n", |
||
| 5150 | " macro avg 0.87 0.59 0.59 578\n", |
||
| 5151 | "weighted avg 0.82 0.78 0.72 578\n", |
||
| 5152 | "\n", |
||
| 5153 | "ROC_AUC_Score: 0.5923663539998474\n" |
||
| 5154 | ] |
||
| 5155 | }, |
||
| 5156 | { |
||
| 5157 | "output_type": "display_data", |
||
| 5158 | "data": { |
||
| 5159 | "text/plain": [ |
||
| 5160 | "<Figure size 700x500 with 2 Axes>" |
||
| 5161 | ], |
||
| 5162 | "image/png": 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\n" |
||
| 5163 | }, |
||
| 5164 | "metadata": {} |
||
| 5165 | } |
||
| 5166 | ] |
||
| 5167 | }, |
||
| 5168 | { |
||
| 5169 | "cell_type": "markdown", |
||
| 5170 | "source": [ |
||
| 5171 | "**Additional ICU Scenarios**" |
||
| 5172 | ], |
||
| 5173 | "metadata": { |
||
| 5174 | "id": "nmT9NaToyJ2b" |
||
| 5175 | } |
||
| 5176 | }, |
||
| 5177 | { |
||
| 5178 | "cell_type": "code", |
||
| 5179 | "source": [ |
||
| 5180 | "# Remove patients admitted to ICU in windows 4-6, 6-12, and above 12\n", |
||
| 5181 | "model_data = filled_data.copy()\n", |
||
| 5182 | "\n", |
||
| 5183 | "remove_window_234 = model_data[(model_data['WINDOW'] == 2)|(model_data['WINDOW'] == 3)|(model_data['WINDOW'] == 4) & (model_data['ICU'] == 1)].index\n", |
||
| 5184 | "model_data.drop(remove_window_234, inplace=True)\n", |
||
| 5185 | "\n", |
||
| 5186 | "model_data.head(15)" |
||
| 5187 | ], |
||
| 5188 | "metadata": { |
||
| 5189 | "id": "YTiqgKeMGpyi", |
||
| 5190 | "colab": { |
||
| 5191 | "base_uri": "https://localhost:8080/", |
||
| 5192 | "height": 648 |
||
| 5193 | }, |
||
| 5194 | "outputId": "3c55a0d6-0cbd-4c95-debd-57fa22569177" |
||
| 5195 | }, |
||
| 5196 | "execution_count": 38, |
||
| 5197 | "outputs": [ |
||
| 5198 | { |
||
| 5199 | "output_type": "execute_result", |
||
| 5200 | "data": { |
||
| 5201 | "text/plain": [ |
||
| 5202 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 5203 | "0 0 1 5 0 \n", |
||
| 5204 | "1 0 1 5 0 \n", |
||
| 5205 | "5 1 1 8 1 \n", |
||
| 5206 | "6 1 1 8 1 \n", |
||
| 5207 | "10 2 0 0 0 \n", |
||
| 5208 | "11 2 0 0 0 \n", |
||
| 5209 | "15 3 0 3 1 \n", |
||
| 5210 | "16 3 0 3 1 \n", |
||
| 5211 | "19 3 0 3 1 \n", |
||
| 5212 | "20 4 0 0 0 \n", |
||
| 5213 | "21 4 0 0 0 \n", |
||
| 5214 | "24 4 0 0 0 \n", |
||
| 5215 | "25 5 0 0 0 \n", |
||
| 5216 | "26 5 0 0 0 \n", |
||
| 5217 | "29 5 0 0 0 \n", |
||
| 5218 | "\n", |
||
| 5219 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 5220 | "0 0.0 0.0 0.0 \n", |
||
| 5221 | "1 0.0 0.0 0.0 \n", |
||
| 5222 | "5 0.0 0.0 0.0 \n", |
||
| 5223 | "6 0.0 0.0 0.0 \n", |
||
| 5224 | "10 0.0 0.0 0.0 \n", |
||
| 5225 | "11 0.0 0.0 0.0 \n", |
||
| 5226 | "15 0.0 0.0 0.0 \n", |
||
| 5227 | "16 0.0 0.0 0.0 \n", |
||
| 5228 | "19 0.0 0.0 0.0 \n", |
||
| 5229 | "20 0.0 0.0 0.0 \n", |
||
| 5230 | "21 0.0 0.0 0.0 \n", |
||
| 5231 | "24 0.0 0.0 0.0 \n", |
||
| 5232 | "25 0.0 0.0 0.0 \n", |
||
| 5233 | "26 0.0 0.0 0.0 \n", |
||
| 5234 | "29 0.0 0.0 0.0 \n", |
||
| 5235 | "\n", |
||
| 5236 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 5237 | "0 0.0 1.0 1.0 ... \n", |
||
| 5238 | "1 0.0 1.0 1.0 ... \n", |
||
| 5239 | "5 0.0 0.0 0.0 ... \n", |
||
| 5240 | "6 0.0 0.0 0.0 ... \n", |
||
| 5241 | "10 0.0 0.0 0.0 ... \n", |
||
| 5242 | "11 0.0 0.0 0.0 ... \n", |
||
| 5243 | "15 0.0 0.0 0.0 ... \n", |
||
| 5244 | "16 0.0 0.0 0.0 ... \n", |
||
| 5245 | "19 0.0 0.0 0.0 ... \n", |
||
| 5246 | "20 0.0 0.0 0.0 ... \n", |
||
| 5247 | "21 0.0 0.0 0.0 ... \n", |
||
| 5248 | "24 0.0 0.0 0.0 ... \n", |
||
| 5249 | "25 0.0 0.0 0.0 ... \n", |
||
| 5250 | "26 0.0 0.0 0.0 ... \n", |
||
| 5251 | "29 0.0 0.0 0.0 ... \n", |
||
| 5252 | "\n", |
||
| 5253 | " OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n", |
||
| 5254 | "0 -1.000000 -1.000000 \n", |
||
| 5255 | "1 -1.000000 -1.000000 \n", |
||
| 5256 | "5 -1.000000 -1.000000 \n", |
||
| 5257 | "6 -1.000000 -1.000000 \n", |
||
| 5258 | "10 -0.959596 -0.515528 \n", |
||
| 5259 | "11 -0.959596 -0.515528 \n", |
||
| 5260 | "15 -1.000000 -1.000000 \n", |
||
| 5261 | "16 -1.000000 -1.000000 \n", |
||
| 5262 | "19 -0.171717 -0.308696 \n", |
||
| 5263 | "20 -0.979798 -1.000000 \n", |
||
| 5264 | "21 -0.979798 -1.000000 \n", |
||
| 5265 | "24 -0.939394 -0.652174 \n", |
||
| 5266 | "25 -0.979798 -0.860870 \n", |
||
| 5267 | "26 -0.979798 -0.860870 \n", |
||
| 5268 | "29 -0.919192 -0.758651 \n", |
||
| 5269 | "\n", |
||
| 5270 | " BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n", |
||
| 5271 | "0 -1.000000 -1.000000 \n", |
||
| 5272 | "1 -1.000000 -1.000000 \n", |
||
| 5273 | "5 -1.000000 -1.000000 \n", |
||
| 5274 | "6 -1.000000 -1.000000 \n", |
||
| 5275 | "10 -0.351328 -0.747001 \n", |
||
| 5276 | "11 -0.351328 -0.747001 \n", |
||
| 5277 | "15 -1.000000 -1.000000 \n", |
||
| 5278 | "16 -1.000000 -1.000000 \n", |
||
| 5279 | "19 -0.057718 -0.069094 \n", |
||
| 5280 | "20 -0.883669 -0.956805 \n", |
||
| 5281 | "21 -0.883669 -0.956805 \n", |
||
| 5282 | "24 -0.596165 -0.634847 \n", |
||
| 5283 | "25 -0.714460 -0.986481 \n", |
||
| 5284 | "26 -0.714460 -0.986481 \n", |
||
| 5285 | "29 -0.683267 -0.581849 \n", |
||
| 5286 | "\n", |
||
| 5287 | " RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 5288 | "0 -1.000000 -1.000000 \n", |
||
| 5289 | "1 -1.000000 -1.000000 \n", |
||
| 5290 | "5 -1.000000 -1.000000 \n", |
||
| 5291 | "6 -1.000000 -1.000000 \n", |
||
| 5292 | "10 -0.756272 -1.000000 \n", |
||
| 5293 | "11 -0.756272 -1.000000 \n", |
||
| 5294 | "15 -1.000000 -1.000000 \n", |
||
| 5295 | "16 -1.000000 -1.000000 \n", |
||
| 5296 | "19 -0.329749 -0.047619 \n", |
||
| 5297 | "20 -0.870968 -0.953536 \n", |
||
| 5298 | "21 -0.870968 -0.953536 \n", |
||
| 5299 | "24 -0.817204 -0.645793 \n", |
||
| 5300 | "25 -1.000000 -0.975891 \n", |
||
| 5301 | "26 -1.000000 -0.975891 \n", |
||
| 5302 | "29 -0.939068 -0.736640 \n", |
||
| 5303 | "\n", |
||
| 5304 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU ICU_SUM \n", |
||
| 5305 | "0 -1.000000 0 0 1 \n", |
||
| 5306 | "1 -1.000000 1 0 1 \n", |
||
| 5307 | "5 -1.000000 0 1 1 \n", |
||
| 5308 | "6 -1.000000 1 1 1 \n", |
||
| 5309 | "10 -0.961262 0 0 1 \n", |
||
| 5310 | "11 -0.961262 1 0 1 \n", |
||
| 5311 | "15 -1.000000 0 0 0 \n", |
||
| 5312 | "16 -1.000000 1 0 0 \n", |
||
| 5313 | "19 -0.172436 4 0 0 \n", |
||
| 5314 | "20 -0.980333 0 0 0 \n", |
||
| 5315 | "21 -0.980333 1 0 0 \n", |
||
| 5316 | "24 -0.940077 4 0 0 \n", |
||
| 5317 | "25 -0.980129 0 0 0 \n", |
||
| 5318 | "26 -0.980129 1 0 0 \n", |
||
| 5319 | "29 -0.920927 4 0 0 \n", |
||
| 5320 | "\n", |
||
| 5321 | "[15 rows x 232 columns]" |
||
| 5322 | ], |
||
| 5323 | "text/html": [ |
||
| 5324 | "\n", |
||
| 5325 | " <div id=\"df-c13dfe4f-f27e-4329-b1fe-bd790fb63888\">\n", |
||
| 5326 | " <div class=\"colab-df-container\">\n", |
||
| 5327 | " <div>\n", |
||
| 5328 | "<style scoped>\n", |
||
| 5329 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 5330 | " vertical-align: middle;\n", |
||
| 5331 | " }\n", |
||
| 5332 | "\n", |
||
| 5333 | " .dataframe tbody tr th {\n", |
||
| 5334 | " vertical-align: top;\n", |
||
| 5335 | " }\n", |
||
| 5336 | "\n", |
||
| 5337 | " .dataframe thead th {\n", |
||
| 5338 | " text-align: right;\n", |
||
| 5339 | " }\n", |
||
| 5340 | "</style>\n", |
||
| 5341 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 5342 | " <thead>\n", |
||
| 5343 | " <tr style=\"text-align: right;\">\n", |
||
| 5344 | " <th></th>\n", |
||
| 5345 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 5346 | " <th>AGE_ABOVE65</th>\n", |
||
| 5347 | " <th>AGE_PERCENTIL</th>\n", |
||
| 5348 | " <th>GENDER</th>\n", |
||
| 5349 | " <th>DISEASE GROUPING 1</th>\n", |
||
| 5350 | " <th>DISEASE GROUPING 2</th>\n", |
||
| 5351 | " <th>DISEASE GROUPING 3</th>\n", |
||
| 5352 | " <th>DISEASE GROUPING 4</th>\n", |
||
| 5353 | " <th>DISEASE GROUPING 5</th>\n", |
||
| 5354 | " <th>DISEASE GROUPING 6</th>\n", |
||
| 5355 | " <th>...</th>\n", |
||
| 5356 | " <th>OXYGEN_SATURATION_DIFF</th>\n", |
||
| 5357 | " <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n", |
||
| 5358 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
||
| 5359 | " <th>HEART_RATE_DIFF_REL</th>\n", |
||
| 5360 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
||
| 5361 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
||
| 5362 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
||
| 5363 | " <th>WINDOW</th>\n", |
||
| 5364 | " <th>ICU</th>\n", |
||
| 5365 | " <th>ICU_SUM</th>\n", |
||
| 5366 | " </tr>\n", |
||
| 5367 | " </thead>\n", |
||
| 5368 | " <tbody>\n", |
||
| 5369 | " <tr>\n", |
||
| 5370 | " <th>0</th>\n", |
||
| 5371 | " <td>0</td>\n", |
||
| 5372 | " <td>1</td>\n", |
||
| 5373 | " <td>5</td>\n", |
||
| 5374 | " <td>0</td>\n", |
||
| 5375 | " <td>0.0</td>\n", |
||
| 5376 | " <td>0.0</td>\n", |
||
| 5377 | " <td>0.0</td>\n", |
||
| 5378 | " <td>0.0</td>\n", |
||
| 5379 | " <td>1.0</td>\n", |
||
| 5380 | " <td>1.0</td>\n", |
||
| 5381 | " <td>...</td>\n", |
||
| 5382 | " <td>-1.000000</td>\n", |
||
| 5383 | " <td>-1.000000</td>\n", |
||
| 5384 | " <td>-1.000000</td>\n", |
||
| 5385 | " <td>-1.000000</td>\n", |
||
| 5386 | " <td>-1.000000</td>\n", |
||
| 5387 | " <td>-1.000000</td>\n", |
||
| 5388 | " <td>-1.000000</td>\n", |
||
| 5389 | " <td>0</td>\n", |
||
| 5390 | " <td>0</td>\n", |
||
| 5391 | " <td>1</td>\n", |
||
| 5392 | " </tr>\n", |
||
| 5393 | " <tr>\n", |
||
| 5394 | " <th>1</th>\n", |
||
| 5395 | " <td>0</td>\n", |
||
| 5396 | " <td>1</td>\n", |
||
| 5397 | " <td>5</td>\n", |
||
| 5398 | " <td>0</td>\n", |
||
| 5399 | " <td>0.0</td>\n", |
||
| 5400 | " <td>0.0</td>\n", |
||
| 5401 | " <td>0.0</td>\n", |
||
| 5402 | " <td>0.0</td>\n", |
||
| 5403 | " <td>1.0</td>\n", |
||
| 5404 | " <td>1.0</td>\n", |
||
| 5405 | " <td>...</td>\n", |
||
| 5406 | " <td>-1.000000</td>\n", |
||
| 5407 | " <td>-1.000000</td>\n", |
||
| 5408 | " <td>-1.000000</td>\n", |
||
| 5409 | " <td>-1.000000</td>\n", |
||
| 5410 | " <td>-1.000000</td>\n", |
||
| 5411 | " <td>-1.000000</td>\n", |
||
| 5412 | " <td>-1.000000</td>\n", |
||
| 5413 | " <td>1</td>\n", |
||
| 5414 | " <td>0</td>\n", |
||
| 5415 | " <td>1</td>\n", |
||
| 5416 | " </tr>\n", |
||
| 5417 | " <tr>\n", |
||
| 5418 | " <th>5</th>\n", |
||
| 5419 | " <td>1</td>\n", |
||
| 5420 | " <td>1</td>\n", |
||
| 5421 | " <td>8</td>\n", |
||
| 5422 | " <td>1</td>\n", |
||
| 5423 | " <td>0.0</td>\n", |
||
| 5424 | " <td>0.0</td>\n", |
||
| 5425 | " <td>0.0</td>\n", |
||
| 5426 | " <td>0.0</td>\n", |
||
| 5427 | " <td>0.0</td>\n", |
||
| 5428 | " <td>0.0</td>\n", |
||
| 5429 | " <td>...</td>\n", |
||
| 5430 | " <td>-1.000000</td>\n", |
||
| 5431 | " <td>-1.000000</td>\n", |
||
| 5432 | " <td>-1.000000</td>\n", |
||
| 5433 | " <td>-1.000000</td>\n", |
||
| 5434 | " <td>-1.000000</td>\n", |
||
| 5435 | " <td>-1.000000</td>\n", |
||
| 5436 | " <td>-1.000000</td>\n", |
||
| 5437 | " <td>0</td>\n", |
||
| 5438 | " <td>1</td>\n", |
||
| 5439 | " <td>1</td>\n", |
||
| 5440 | " </tr>\n", |
||
| 5441 | " <tr>\n", |
||
| 5442 | " <th>6</th>\n", |
||
| 5443 | " <td>1</td>\n", |
||
| 5444 | " <td>1</td>\n", |
||
| 5445 | " <td>8</td>\n", |
||
| 5446 | " <td>1</td>\n", |
||
| 5447 | " <td>0.0</td>\n", |
||
| 5448 | " <td>0.0</td>\n", |
||
| 5449 | " <td>0.0</td>\n", |
||
| 5450 | " <td>0.0</td>\n", |
||
| 5451 | " <td>0.0</td>\n", |
||
| 5452 | " <td>0.0</td>\n", |
||
| 5453 | " <td>...</td>\n", |
||
| 5454 | " <td>-1.000000</td>\n", |
||
| 5455 | " <td>-1.000000</td>\n", |
||
| 5456 | " <td>-1.000000</td>\n", |
||
| 5457 | " <td>-1.000000</td>\n", |
||
| 5458 | " <td>-1.000000</td>\n", |
||
| 5459 | " <td>-1.000000</td>\n", |
||
| 5460 | " <td>-1.000000</td>\n", |
||
| 5461 | " <td>1</td>\n", |
||
| 5462 | " <td>1</td>\n", |
||
| 5463 | " <td>1</td>\n", |
||
| 5464 | " </tr>\n", |
||
| 5465 | " <tr>\n", |
||
| 5466 | " <th>10</th>\n", |
||
| 5467 | " <td>2</td>\n", |
||
| 5468 | " <td>0</td>\n", |
||
| 5469 | " <td>0</td>\n", |
||
| 5470 | " <td>0</td>\n", |
||
| 5471 | " <td>0.0</td>\n", |
||
| 5472 | " <td>0.0</td>\n", |
||
| 5473 | " <td>0.0</td>\n", |
||
| 5474 | " <td>0.0</td>\n", |
||
| 5475 | " <td>0.0</td>\n", |
||
| 5476 | " <td>0.0</td>\n", |
||
| 5477 | " <td>...</td>\n", |
||
| 5478 | " <td>-0.959596</td>\n", |
||
| 5479 | " <td>-0.515528</td>\n", |
||
| 5480 | " <td>-0.351328</td>\n", |
||
| 5481 | " <td>-0.747001</td>\n", |
||
| 5482 | " <td>-0.756272</td>\n", |
||
| 5483 | " <td>-1.000000</td>\n", |
||
| 5484 | " <td>-0.961262</td>\n", |
||
| 5485 | " <td>0</td>\n", |
||
| 5486 | " <td>0</td>\n", |
||
| 5487 | " <td>1</td>\n", |
||
| 5488 | " </tr>\n", |
||
| 5489 | " <tr>\n", |
||
| 5490 | " <th>11</th>\n", |
||
| 5491 | " <td>2</td>\n", |
||
| 5492 | " <td>0</td>\n", |
||
| 5493 | " <td>0</td>\n", |
||
| 5494 | " <td>0</td>\n", |
||
| 5495 | " <td>0.0</td>\n", |
||
| 5496 | " <td>0.0</td>\n", |
||
| 5497 | " <td>0.0</td>\n", |
||
| 5498 | " <td>0.0</td>\n", |
||
| 5499 | " <td>0.0</td>\n", |
||
| 5500 | " <td>0.0</td>\n", |
||
| 5501 | " <td>...</td>\n", |
||
| 5502 | " <td>-0.959596</td>\n", |
||
| 5503 | " <td>-0.515528</td>\n", |
||
| 5504 | " <td>-0.351328</td>\n", |
||
| 5505 | " <td>-0.747001</td>\n", |
||
| 5506 | " <td>-0.756272</td>\n", |
||
| 5507 | " <td>-1.000000</td>\n", |
||
| 5508 | " <td>-0.961262</td>\n", |
||
| 5509 | " <td>1</td>\n", |
||
| 5510 | " <td>0</td>\n", |
||
| 5511 | " <td>1</td>\n", |
||
| 5512 | " </tr>\n", |
||
| 5513 | " <tr>\n", |
||
| 5514 | " <th>15</th>\n", |
||
| 5515 | " <td>3</td>\n", |
||
| 5516 | " <td>0</td>\n", |
||
| 5517 | " <td>3</td>\n", |
||
| 5518 | " <td>1</td>\n", |
||
| 5519 | " <td>0.0</td>\n", |
||
| 5520 | " <td>0.0</td>\n", |
||
| 5521 | " <td>0.0</td>\n", |
||
| 5522 | " <td>0.0</td>\n", |
||
| 5523 | " <td>0.0</td>\n", |
||
| 5524 | " <td>0.0</td>\n", |
||
| 5525 | " <td>...</td>\n", |
||
| 5526 | " <td>-1.000000</td>\n", |
||
| 5527 | " <td>-1.000000</td>\n", |
||
| 5528 | " <td>-1.000000</td>\n", |
||
| 5529 | " <td>-1.000000</td>\n", |
||
| 5530 | " <td>-1.000000</td>\n", |
||
| 5531 | " <td>-1.000000</td>\n", |
||
| 5532 | " <td>-1.000000</td>\n", |
||
| 5533 | " <td>0</td>\n", |
||
| 5534 | " <td>0</td>\n", |
||
| 5535 | " <td>0</td>\n", |
||
| 5536 | " </tr>\n", |
||
| 5537 | " <tr>\n", |
||
| 5538 | " <th>16</th>\n", |
||
| 5539 | " <td>3</td>\n", |
||
| 5540 | " <td>0</td>\n", |
||
| 5541 | " <td>3</td>\n", |
||
| 5542 | " <td>1</td>\n", |
||
| 5543 | " <td>0.0</td>\n", |
||
| 5544 | " <td>0.0</td>\n", |
||
| 5545 | " <td>0.0</td>\n", |
||
| 5546 | " <td>0.0</td>\n", |
||
| 5547 | " <td>0.0</td>\n", |
||
| 5548 | " <td>0.0</td>\n", |
||
| 5549 | " <td>...</td>\n", |
||
| 5550 | " <td>-1.000000</td>\n", |
||
| 5551 | " <td>-1.000000</td>\n", |
||
| 5552 | " <td>-1.000000</td>\n", |
||
| 5553 | " <td>-1.000000</td>\n", |
||
| 5554 | " <td>-1.000000</td>\n", |
||
| 5555 | " <td>-1.000000</td>\n", |
||
| 5556 | " <td>-1.000000</td>\n", |
||
| 5557 | " <td>1</td>\n", |
||
| 5558 | " <td>0</td>\n", |
||
| 5559 | " <td>0</td>\n", |
||
| 5560 | " </tr>\n", |
||
| 5561 | " <tr>\n", |
||
| 5562 | " <th>19</th>\n", |
||
| 5563 | " <td>3</td>\n", |
||
| 5564 | " <td>0</td>\n", |
||
| 5565 | " <td>3</td>\n", |
||
| 5566 | " <td>1</td>\n", |
||
| 5567 | " <td>0.0</td>\n", |
||
| 5568 | " <td>0.0</td>\n", |
||
| 5569 | " <td>0.0</td>\n", |
||
| 5570 | " <td>0.0</td>\n", |
||
| 5571 | " <td>0.0</td>\n", |
||
| 5572 | " <td>0.0</td>\n", |
||
| 5573 | " <td>...</td>\n", |
||
| 5574 | " <td>-0.171717</td>\n", |
||
| 5575 | " <td>-0.308696</td>\n", |
||
| 5576 | " <td>-0.057718</td>\n", |
||
| 5577 | " <td>-0.069094</td>\n", |
||
| 5578 | " <td>-0.329749</td>\n", |
||
| 5579 | " <td>-0.047619</td>\n", |
||
| 5580 | " <td>-0.172436</td>\n", |
||
| 5581 | " <td>4</td>\n", |
||
| 5582 | " <td>0</td>\n", |
||
| 5583 | " <td>0</td>\n", |
||
| 5584 | " </tr>\n", |
||
| 5585 | " <tr>\n", |
||
| 5586 | " <th>20</th>\n", |
||
| 5587 | " <td>4</td>\n", |
||
| 5588 | " <td>0</td>\n", |
||
| 5589 | " <td>0</td>\n", |
||
| 5590 | " <td>0</td>\n", |
||
| 5591 | " <td>0.0</td>\n", |
||
| 5592 | " <td>0.0</td>\n", |
||
| 5593 | " <td>0.0</td>\n", |
||
| 5594 | " <td>0.0</td>\n", |
||
| 5595 | " <td>0.0</td>\n", |
||
| 5596 | " <td>0.0</td>\n", |
||
| 5597 | " <td>...</td>\n", |
||
| 5598 | " <td>-0.979798</td>\n", |
||
| 5599 | " <td>-1.000000</td>\n", |
||
| 5600 | " <td>-0.883669</td>\n", |
||
| 5601 | " <td>-0.956805</td>\n", |
||
| 5602 | " <td>-0.870968</td>\n", |
||
| 5603 | " <td>-0.953536</td>\n", |
||
| 5604 | " <td>-0.980333</td>\n", |
||
| 5605 | " <td>0</td>\n", |
||
| 5606 | " <td>0</td>\n", |
||
| 5607 | " <td>0</td>\n", |
||
| 5608 | " </tr>\n", |
||
| 5609 | " <tr>\n", |
||
| 5610 | " <th>21</th>\n", |
||
| 5611 | " <td>4</td>\n", |
||
| 5612 | " <td>0</td>\n", |
||
| 5613 | " <td>0</td>\n", |
||
| 5614 | " <td>0</td>\n", |
||
| 5615 | " <td>0.0</td>\n", |
||
| 5616 | " <td>0.0</td>\n", |
||
| 5617 | " <td>0.0</td>\n", |
||
| 5618 | " <td>0.0</td>\n", |
||
| 5619 | " <td>0.0</td>\n", |
||
| 5620 | " <td>0.0</td>\n", |
||
| 5621 | " <td>...</td>\n", |
||
| 5622 | " <td>-0.979798</td>\n", |
||
| 5623 | " <td>-1.000000</td>\n", |
||
| 5624 | " <td>-0.883669</td>\n", |
||
| 5625 | " <td>-0.956805</td>\n", |
||
| 5626 | " <td>-0.870968</td>\n", |
||
| 5627 | " <td>-0.953536</td>\n", |
||
| 5628 | " <td>-0.980333</td>\n", |
||
| 5629 | " <td>1</td>\n", |
||
| 5630 | " <td>0</td>\n", |
||
| 5631 | " <td>0</td>\n", |
||
| 5632 | " </tr>\n", |
||
| 5633 | " <tr>\n", |
||
| 5634 | " <th>24</th>\n", |
||
| 5635 | " <td>4</td>\n", |
||
| 5636 | " <td>0</td>\n", |
||
| 5637 | " <td>0</td>\n", |
||
| 5638 | " <td>0</td>\n", |
||
| 5639 | " <td>0.0</td>\n", |
||
| 5640 | " <td>0.0</td>\n", |
||
| 5641 | " <td>0.0</td>\n", |
||
| 5642 | " <td>0.0</td>\n", |
||
| 5643 | " <td>0.0</td>\n", |
||
| 5644 | " <td>0.0</td>\n", |
||
| 5645 | " <td>...</td>\n", |
||
| 5646 | " <td>-0.939394</td>\n", |
||
| 5647 | " <td>-0.652174</td>\n", |
||
| 5648 | " <td>-0.596165</td>\n", |
||
| 5649 | " <td>-0.634847</td>\n", |
||
| 5650 | " <td>-0.817204</td>\n", |
||
| 5651 | " <td>-0.645793</td>\n", |
||
| 5652 | " <td>-0.940077</td>\n", |
||
| 5653 | " <td>4</td>\n", |
||
| 5654 | " <td>0</td>\n", |
||
| 5655 | " <td>0</td>\n", |
||
| 5656 | " </tr>\n", |
||
| 5657 | " <tr>\n", |
||
| 5658 | " <th>25</th>\n", |
||
| 5659 | " <td>5</td>\n", |
||
| 5660 | " <td>0</td>\n", |
||
| 5661 | " <td>0</td>\n", |
||
| 5662 | " <td>0</td>\n", |
||
| 5663 | " <td>0.0</td>\n", |
||
| 5664 | " <td>0.0</td>\n", |
||
| 5665 | " <td>0.0</td>\n", |
||
| 5666 | " <td>0.0</td>\n", |
||
| 5667 | " <td>0.0</td>\n", |
||
| 5668 | " <td>0.0</td>\n", |
||
| 5669 | " <td>...</td>\n", |
||
| 5670 | " <td>-0.979798</td>\n", |
||
| 5671 | " <td>-0.860870</td>\n", |
||
| 5672 | " <td>-0.714460</td>\n", |
||
| 5673 | " <td>-0.986481</td>\n", |
||
| 5674 | " <td>-1.000000</td>\n", |
||
| 5675 | " <td>-0.975891</td>\n", |
||
| 5676 | " <td>-0.980129</td>\n", |
||
| 5677 | " <td>0</td>\n", |
||
| 5678 | " <td>0</td>\n", |
||
| 5679 | " <td>0</td>\n", |
||
| 5680 | " </tr>\n", |
||
| 5681 | " <tr>\n", |
||
| 5682 | " <th>26</th>\n", |
||
| 5683 | " <td>5</td>\n", |
||
| 5684 | " <td>0</td>\n", |
||
| 5685 | " <td>0</td>\n", |
||
| 5686 | " <td>0</td>\n", |
||
| 5687 | " <td>0.0</td>\n", |
||
| 5688 | " <td>0.0</td>\n", |
||
| 5689 | " <td>0.0</td>\n", |
||
| 5690 | " <td>0.0</td>\n", |
||
| 5691 | " <td>0.0</td>\n", |
||
| 5692 | " <td>0.0</td>\n", |
||
| 5693 | " <td>...</td>\n", |
||
| 5694 | " <td>-0.979798</td>\n", |
||
| 5695 | " <td>-0.860870</td>\n", |
||
| 5696 | " <td>-0.714460</td>\n", |
||
| 5697 | " <td>-0.986481</td>\n", |
||
| 5698 | " <td>-1.000000</td>\n", |
||
| 5699 | " <td>-0.975891</td>\n", |
||
| 5700 | " <td>-0.980129</td>\n", |
||
| 5701 | " <td>1</td>\n", |
||
| 5702 | " <td>0</td>\n", |
||
| 5703 | " <td>0</td>\n", |
||
| 5704 | " </tr>\n", |
||
| 5705 | " <tr>\n", |
||
| 5706 | " <th>29</th>\n", |
||
| 5707 | " <td>5</td>\n", |
||
| 5708 | " <td>0</td>\n", |
||
| 5709 | " <td>0</td>\n", |
||
| 5710 | " <td>0</td>\n", |
||
| 5711 | " <td>0.0</td>\n", |
||
| 5712 | " <td>0.0</td>\n", |
||
| 5713 | " <td>0.0</td>\n", |
||
| 5714 | " <td>0.0</td>\n", |
||
| 5715 | " <td>0.0</td>\n", |
||
| 5716 | " <td>0.0</td>\n", |
||
| 5717 | " <td>...</td>\n", |
||
| 5718 | " <td>-0.919192</td>\n", |
||
| 5719 | " <td>-0.758651</td>\n", |
||
| 5720 | " <td>-0.683267</td>\n", |
||
| 5721 | " <td>-0.581849</td>\n", |
||
| 5722 | " <td>-0.939068</td>\n", |
||
| 5723 | " <td>-0.736640</td>\n", |
||
| 5724 | " <td>-0.920927</td>\n", |
||
| 5725 | " <td>4</td>\n", |
||
| 5726 | " <td>0</td>\n", |
||
| 5727 | " <td>0</td>\n", |
||
| 5728 | " </tr>\n", |
||
| 5729 | " </tbody>\n", |
||
| 5730 | "</table>\n", |
||
| 5731 | "<p>15 rows × 232 columns</p>\n", |
||
| 5732 | "</div>\n", |
||
| 5733 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c13dfe4f-f27e-4329-b1fe-bd790fb63888')\"\n", |
||
| 5734 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 5735 | " style=\"display:none;\">\n", |
||
| 5736 | " \n", |
||
| 5737 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 5738 | " width=\"24px\">\n", |
||
| 5739 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 5740 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 5741 | " </svg>\n", |
||
| 5742 | " </button>\n", |
||
| 5743 | " \n", |
||
| 5744 | " <style>\n", |
||
| 5745 | " .colab-df-container {\n", |
||
| 5746 | " display:flex;\n", |
||
| 5747 | " flex-wrap:wrap;\n", |
||
| 5748 | " gap: 12px;\n", |
||
| 5749 | " }\n", |
||
| 5750 | "\n", |
||
| 5751 | " .colab-df-convert {\n", |
||
| 5752 | " background-color: #E8F0FE;\n", |
||
| 5753 | " border: none;\n", |
||
| 5754 | " border-radius: 50%;\n", |
||
| 5755 | " cursor: pointer;\n", |
||
| 5756 | " display: none;\n", |
||
| 5757 | " fill: #1967D2;\n", |
||
| 5758 | " height: 32px;\n", |
||
| 5759 | " padding: 0 0 0 0;\n", |
||
| 5760 | " width: 32px;\n", |
||
| 5761 | " }\n", |
||
| 5762 | "\n", |
||
| 5763 | " .colab-df-convert:hover {\n", |
||
| 5764 | " background-color: #E2EBFA;\n", |
||
| 5765 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 5766 | " fill: #174EA6;\n", |
||
| 5767 | " }\n", |
||
| 5768 | "\n", |
||
| 5769 | " [theme=dark] .colab-df-convert {\n", |
||
| 5770 | " background-color: #3B4455;\n", |
||
| 5771 | " fill: #D2E3FC;\n", |
||
| 5772 | " }\n", |
||
| 5773 | "\n", |
||
| 5774 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 5775 | " background-color: #434B5C;\n", |
||
| 5776 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 5777 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 5778 | " fill: #FFFFFF;\n", |
||
| 5779 | " }\n", |
||
| 5780 | " </style>\n", |
||
| 5781 | "\n", |
||
| 5782 | " <script>\n", |
||
| 5783 | " const buttonEl =\n", |
||
| 5784 | " document.querySelector('#df-c13dfe4f-f27e-4329-b1fe-bd790fb63888 button.colab-df-convert');\n", |
||
| 5785 | " buttonEl.style.display =\n", |
||
| 5786 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 5787 | "\n", |
||
| 5788 | " async function convertToInteractive(key) {\n", |
||
| 5789 | " const element = document.querySelector('#df-c13dfe4f-f27e-4329-b1fe-bd790fb63888');\n", |
||
| 5790 | " const dataTable =\n", |
||
| 5791 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 5792 | " [key], {});\n", |
||
| 5793 | " if (!dataTable) return;\n", |
||
| 5794 | "\n", |
||
| 5795 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 5796 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 5797 | " + ' to learn more about interactive tables.';\n", |
||
| 5798 | " element.innerHTML = '';\n", |
||
| 5799 | " dataTable['output_type'] = 'display_data';\n", |
||
| 5800 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 5801 | " const docLink = document.createElement('div');\n", |
||
| 5802 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 5803 | " element.appendChild(docLink);\n", |
||
| 5804 | " }\n", |
||
| 5805 | " </script>\n", |
||
| 5806 | " </div>\n", |
||
| 5807 | " </div>\n", |
||
| 5808 | " " |
||
| 5809 | ] |
||
| 5810 | }, |
||
| 5811 | "metadata": {}, |
||
| 5812 | "execution_count": 38 |
||
| 5813 | } |
||
| 5814 | ] |
||
| 5815 | }, |
||
| 5816 | { |
||
| 5817 | "cell_type": "code", |
||
| 5818 | "source": [ |
||
| 5819 | "score_remove_window_234 = log_reg_model(model_data)" |
||
| 5820 | ], |
||
| 5821 | "metadata": { |
||
| 5822 | "id": "Z58KrPmQGuRa", |
||
| 5823 | "colab": { |
||
| 5824 | "base_uri": "https://localhost:8080/", |
||
| 5825 | "height": 725 |
||
| 5826 | }, |
||
| 5827 | "outputId": "efd7189d-0805-4e21-a836-e5cb4311218b" |
||
| 5828 | }, |
||
| 5829 | "execution_count": 39, |
||
| 5830 | "outputs": [ |
||
| 5831 | { |
||
| 5832 | "output_type": "stream", |
||
| 5833 | "name": "stdout", |
||
| 5834 | "text": [ |
||
| 5835 | "Training data: (1347, 231)\n", |
||
| 5836 | "Test data: (578, 231)\n", |
||
| 5837 | "\n" |
||
| 5838 | ] |
||
| 5839 | }, |
||
| 5840 | { |
||
| 5841 | "output_type": "stream", |
||
| 5842 | "name": "stderr", |
||
| 5843 | "text": [ |
||
| 5844 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", |
||
| 5845 | " y = column_or_1d(y, warn=True)\n" |
||
| 5846 | ] |
||
| 5847 | }, |
||
| 5848 | { |
||
| 5849 | "output_type": "stream", |
||
| 5850 | "name": "stdout", |
||
| 5851 | "text": [ |
||
| 5852 | " precision recall f1-score support\n", |
||
| 5853 | "\n", |
||
| 5854 | " 0 0.93 0.94 0.94 423\n", |
||
| 5855 | " 1 0.84 0.81 0.83 155\n", |
||
| 5856 | "\n", |
||
| 5857 | " accuracy 0.91 578\n", |
||
| 5858 | " macro avg 0.89 0.88 0.88 578\n", |
||
| 5859 | "weighted avg 0.91 0.91 0.91 578\n", |
||
| 5860 | "\n", |
||
| 5861 | "ROC_AUC_Score: 0.8780828185769848\n" |
||
| 5862 | ] |
||
| 5863 | }, |
||
| 5864 | { |
||
| 5865 | "output_type": "display_data", |
||
| 5866 | "data": { |
||
| 5867 | "text/plain": [ |
||
| 5868 | "<Figure size 700x500 with 2 Axes>" |
||
| 5869 | ], |
||
| 5870 | "image/png": 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\n" |
||
| 5871 | }, |
||
| 5872 | "metadata": {} |
||
| 5873 | } |
||
| 5874 | ] |
||
| 5875 | }, |
||
| 5876 | { |
||
| 5877 | "cell_type": "code", |
||
| 5878 | "source": [ |
||
| 5879 | "score_remove_window_234 = decision_tree_model(model_data)" |
||
| 5880 | ], |
||
| 5881 | "metadata": { |
||
| 5882 | "id": "gNeAns_nG0Dz", |
||
| 5883 | "colab": { |
||
| 5884 | "base_uri": "https://localhost:8080/", |
||
| 5885 | "height": 671 |
||
| 5886 | }, |
||
| 5887 | "outputId": "b5d26f62-000f-4d9f-b3ce-b9994f2ef26b" |
||
| 5888 | }, |
||
| 5889 | "execution_count": 40, |
||
| 5890 | "outputs": [ |
||
| 5891 | { |
||
| 5892 | "output_type": "stream", |
||
| 5893 | "name": "stdout", |
||
| 5894 | "text": [ |
||
| 5895 | "Training data: (1347, 231)\n", |
||
| 5896 | "Test data: (578, 231)\n", |
||
| 5897 | "\n", |
||
| 5898 | " precision recall f1-score support\n", |
||
| 5899 | "\n", |
||
| 5900 | " 0 0.93 0.91 0.92 423\n", |
||
| 5901 | " 1 0.77 0.81 0.79 155\n", |
||
| 5902 | "\n", |
||
| 5903 | " accuracy 0.89 578\n", |
||
| 5904 | " macro avg 0.85 0.86 0.86 578\n", |
||
| 5905 | "weighted avg 0.89 0.89 0.89 578\n", |
||
| 5906 | "\n", |
||
| 5907 | "ROC_AUC_Score: 0.8627163883169374\n" |
||
| 5908 | ] |
||
| 5909 | }, |
||
| 5910 | { |
||
| 5911 | "output_type": "display_data", |
||
| 5912 | "data": { |
||
| 5913 | "text/plain": [ |
||
| 5914 | "<Figure size 700x500 with 2 Axes>" |
||
| 5915 | ], |
||
| 5916 | "image/png": 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\n" |
||
| 5917 | }, |
||
| 5918 | "metadata": {} |
||
| 5919 | } |
||
| 5920 | ] |
||
| 5921 | }, |
||
| 5922 | { |
||
| 5923 | "cell_type": "code", |
||
| 5924 | "source": [ |
||
| 5925 | "score_remove_window_234 = k_neighbors_model(model_data)" |
||
| 5926 | ], |
||
| 5927 | "metadata": { |
||
| 5928 | "id": "zNssqgevG5y5", |
||
| 5929 | "colab": { |
||
| 5930 | "base_uri": "https://localhost:8080/", |
||
| 5931 | "height": 725 |
||
| 5932 | }, |
||
| 5933 | "outputId": "5e8f6bb0-95d8-4099-b521-0482883ccb51" |
||
| 5934 | }, |
||
| 5935 | "execution_count": 41, |
||
| 5936 | "outputs": [ |
||
| 5937 | { |
||
| 5938 | "output_type": "stream", |
||
| 5939 | "name": "stdout", |
||
| 5940 | "text": [ |
||
| 5941 | "Training data: (1347, 231)\n", |
||
| 5942 | "Test data: (578, 231)\n", |
||
| 5943 | "\n", |
||
| 5944 | " precision recall f1-score support\n", |
||
| 5945 | "\n", |
||
| 5946 | " 0 0.76 1.00 0.87 423\n", |
||
| 5947 | " 1 1.00 0.15 0.26 155\n", |
||
| 5948 | "\n", |
||
| 5949 | " accuracy 0.77 578\n", |
||
| 5950 | " macro avg 0.88 0.57 0.56 578\n", |
||
| 5951 | "weighted avg 0.83 0.77 0.70 578\n", |
||
| 5952 | "\n", |
||
| 5953 | "ROC_AUC_Score: 0.5741935483870968\n" |
||
| 5954 | ] |
||
| 5955 | }, |
||
| 5956 | { |
||
| 5957 | "output_type": "stream", |
||
| 5958 | "name": "stderr", |
||
| 5959 | "text": [ |
||
| 5960 | "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", |
||
| 5961 | " return self._fit(X, y)\n" |
||
| 5962 | ] |
||
| 5963 | }, |
||
| 5964 | { |
||
| 5965 | "output_type": "display_data", |
||
| 5966 | "data": { |
||
| 5967 | "text/plain": [ |
||
| 5968 | "<Figure size 700x500 with 2 Axes>" |
||
| 5969 | ], |
||
| 5970 | "image/png": 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\n" |
||
| 5971 | }, |
||
| 5972 | "metadata": {} |
||
| 5973 | } |
||
| 5974 | ] |
||
| 5975 | }, |
||
| 5976 | { |
||
| 5977 | "cell_type": "code", |
||
| 5978 | "source": [ |
||
| 5979 | "# Remove patients admitted to ICU in windows 6-12 and above 12\n", |
||
| 5980 | "model_data = filled_data.copy()\n", |
||
| 5981 | "\n", |
||
| 5982 | "remove_window_34 = model_data[(model_data['WINDOW'] == 3)|(model_data['WINDOW'] == 4) & (model_data['ICU'] == 1)].index\n", |
||
| 5983 | "model_data.drop(remove_window_34, inplace=True)\n", |
||
| 5984 | "\n", |
||
| 5985 | "model_data.head(15)" |
||
| 5986 | ], |
||
| 5987 | "metadata": { |
||
| 5988 | "id": "6lS4HmRpGFqY", |
||
| 5989 | "colab": { |
||
| 5990 | "base_uri": "https://localhost:8080/", |
||
| 5991 | "height": 648 |
||
| 5992 | }, |
||
| 5993 | "outputId": "7d0e99fd-dd26-444b-f1f3-ff5eb064c73c" |
||
| 5994 | }, |
||
| 5995 | "execution_count": 42, |
||
| 5996 | "outputs": [ |
||
| 5997 | { |
||
| 5998 | "output_type": "execute_result", |
||
| 5999 | "data": { |
||
| 6000 | "text/plain": [ |
||
| 6001 | " PATIENT_VISIT_IDENTIFIER AGE_ABOVE65 AGE_PERCENTIL GENDER \\\n", |
||
| 6002 | "0 0 1 5 0 \n", |
||
| 6003 | "1 0 1 5 0 \n", |
||
| 6004 | "2 0 1 5 0 \n", |
||
| 6005 | "5 1 1 8 1 \n", |
||
| 6006 | "6 1 1 8 1 \n", |
||
| 6007 | "7 1 1 8 1 \n", |
||
| 6008 | "10 2 0 0 0 \n", |
||
| 6009 | "11 2 0 0 0 \n", |
||
| 6010 | "12 2 0 0 0 \n", |
||
| 6011 | "15 3 0 3 1 \n", |
||
| 6012 | "16 3 0 3 1 \n", |
||
| 6013 | "17 3 0 3 1 \n", |
||
| 6014 | "19 3 0 3 1 \n", |
||
| 6015 | "20 4 0 0 0 \n", |
||
| 6016 | "21 4 0 0 0 \n", |
||
| 6017 | "\n", |
||
| 6018 | " DISEASE GROUPING 1 DISEASE GROUPING 2 DISEASE GROUPING 3 \\\n", |
||
| 6019 | "0 0.0 0.0 0.0 \n", |
||
| 6020 | "1 0.0 0.0 0.0 \n", |
||
| 6021 | "2 0.0 0.0 0.0 \n", |
||
| 6022 | "5 0.0 0.0 0.0 \n", |
||
| 6023 | "6 0.0 0.0 0.0 \n", |
||
| 6024 | "7 0.0 0.0 0.0 \n", |
||
| 6025 | "10 0.0 0.0 0.0 \n", |
||
| 6026 | "11 0.0 0.0 0.0 \n", |
||
| 6027 | "12 0.0 0.0 0.0 \n", |
||
| 6028 | "15 0.0 0.0 0.0 \n", |
||
| 6029 | "16 0.0 0.0 0.0 \n", |
||
| 6030 | "17 0.0 0.0 0.0 \n", |
||
| 6031 | "19 0.0 0.0 0.0 \n", |
||
| 6032 | "20 0.0 0.0 0.0 \n", |
||
| 6033 | "21 0.0 0.0 0.0 \n", |
||
| 6034 | "\n", |
||
| 6035 | " DISEASE GROUPING 4 DISEASE GROUPING 5 DISEASE GROUPING 6 ... \\\n", |
||
| 6036 | "0 0.0 1.0 1.0 ... \n", |
||
| 6037 | "1 0.0 1.0 1.0 ... \n", |
||
| 6038 | "2 0.0 1.0 1.0 ... \n", |
||
| 6039 | "5 0.0 0.0 0.0 ... \n", |
||
| 6040 | "6 0.0 0.0 0.0 ... \n", |
||
| 6041 | "7 0.0 0.0 0.0 ... \n", |
||
| 6042 | "10 0.0 0.0 0.0 ... \n", |
||
| 6043 | "11 0.0 0.0 0.0 ... \n", |
||
| 6044 | "12 0.0 0.0 0.0 ... \n", |
||
| 6045 | "15 0.0 0.0 0.0 ... \n", |
||
| 6046 | "16 0.0 0.0 0.0 ... \n", |
||
| 6047 | "17 0.0 0.0 0.0 ... \n", |
||
| 6048 | "19 0.0 0.0 0.0 ... \n", |
||
| 6049 | "20 0.0 0.0 0.0 ... \n", |
||
| 6050 | "21 0.0 0.0 0.0 ... \n", |
||
| 6051 | "\n", |
||
| 6052 | " OXYGEN_SATURATION_DIFF BLOODPRESSURE_DIASTOLIC_DIFF_REL \\\n", |
||
| 6053 | "0 -1.000000 -1.000000 \n", |
||
| 6054 | "1 -1.000000 -1.000000 \n", |
||
| 6055 | "2 -1.000000 -1.000000 \n", |
||
| 6056 | "5 -1.000000 -1.000000 \n", |
||
| 6057 | "6 -1.000000 -1.000000 \n", |
||
| 6058 | "7 -1.000000 -1.000000 \n", |
||
| 6059 | "10 -0.959596 -0.515528 \n", |
||
| 6060 | "11 -0.959596 -0.515528 \n", |
||
| 6061 | "12 -0.959596 -0.515528 \n", |
||
| 6062 | "15 -1.000000 -1.000000 \n", |
||
| 6063 | "16 -1.000000 -1.000000 \n", |
||
| 6064 | "17 -1.000000 -1.000000 \n", |
||
| 6065 | "19 -0.171717 -0.308696 \n", |
||
| 6066 | "20 -0.979798 -1.000000 \n", |
||
| 6067 | "21 -0.979798 -1.000000 \n", |
||
| 6068 | "\n", |
||
| 6069 | " BLOODPRESSURE_SISTOLIC_DIFF_REL HEART_RATE_DIFF_REL \\\n", |
||
| 6070 | "0 -1.000000 -1.000000 \n", |
||
| 6071 | "1 -1.000000 -1.000000 \n", |
||
| 6072 | "2 -1.000000 -1.000000 \n", |
||
| 6073 | "5 -1.000000 -1.000000 \n", |
||
| 6074 | "6 -1.000000 -1.000000 \n", |
||
| 6075 | "7 -1.000000 -1.000000 \n", |
||
| 6076 | "10 -0.351328 -0.747001 \n", |
||
| 6077 | "11 -0.351328 -0.747001 \n", |
||
| 6078 | "12 -0.351328 -0.747001 \n", |
||
| 6079 | "15 -1.000000 -1.000000 \n", |
||
| 6080 | "16 -1.000000 -1.000000 \n", |
||
| 6081 | "17 -1.000000 -1.000000 \n", |
||
| 6082 | "19 -0.057718 -0.069094 \n", |
||
| 6083 | "20 -0.883669 -0.956805 \n", |
||
| 6084 | "21 -0.883669 -0.956805 \n", |
||
| 6085 | "\n", |
||
| 6086 | " RESPIRATORY_RATE_DIFF_REL TEMPERATURE_DIFF_REL \\\n", |
||
| 6087 | "0 -1.000000 -1.000000 \n", |
||
| 6088 | "1 -1.000000 -1.000000 \n", |
||
| 6089 | "2 -1.000000 -1.000000 \n", |
||
| 6090 | "5 -1.000000 -1.000000 \n", |
||
| 6091 | "6 -1.000000 -1.000000 \n", |
||
| 6092 | "7 -1.000000 -1.000000 \n", |
||
| 6093 | "10 -0.756272 -1.000000 \n", |
||
| 6094 | "11 -0.756272 -1.000000 \n", |
||
| 6095 | "12 -0.756272 -1.000000 \n", |
||
| 6096 | "15 -1.000000 -1.000000 \n", |
||
| 6097 | "16 -1.000000 -1.000000 \n", |
||
| 6098 | "17 -1.000000 -1.000000 \n", |
||
| 6099 | "19 -0.329749 -0.047619 \n", |
||
| 6100 | "20 -0.870968 -0.953536 \n", |
||
| 6101 | "21 -0.870968 -0.953536 \n", |
||
| 6102 | "\n", |
||
| 6103 | " OXYGEN_SATURATION_DIFF_REL WINDOW ICU ICU_SUM \n", |
||
| 6104 | "0 -1.000000 0 0 1 \n", |
||
| 6105 | "1 -1.000000 1 0 1 \n", |
||
| 6106 | "2 -1.000000 2 0 1 \n", |
||
| 6107 | "5 -1.000000 0 1 1 \n", |
||
| 6108 | "6 -1.000000 1 1 1 \n", |
||
| 6109 | "7 -1.000000 2 1 1 \n", |
||
| 6110 | "10 -0.961262 0 0 1 \n", |
||
| 6111 | "11 -0.961262 1 0 1 \n", |
||
| 6112 | "12 -0.961262 2 0 1 \n", |
||
| 6113 | "15 -1.000000 0 0 0 \n", |
||
| 6114 | "16 -1.000000 1 0 0 \n", |
||
| 6115 | "17 -1.000000 2 0 0 \n", |
||
| 6116 | "19 -0.172436 4 0 0 \n", |
||
| 6117 | "20 -0.980333 0 0 0 \n", |
||
| 6118 | "21 -0.980333 1 0 0 \n", |
||
| 6119 | "\n", |
||
| 6120 | "[15 rows x 232 columns]" |
||
| 6121 | ], |
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| 6124 | " <div id=\"df-4344241e-c0bb-4623-a19f-46417310e52a\">\n", |
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| 6125 | " <div class=\"colab-df-container\">\n", |
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| 6126 | " <div>\n", |
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| 6127 | "<style scoped>\n", |
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| 6128 | " .dataframe tbody tr th:only-of-type {\n", |
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| 6129 | " vertical-align: middle;\n", |
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| 6130 | " }\n", |
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| 6131 | "\n", |
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| 6132 | " .dataframe tbody tr th {\n", |
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| 6133 | " vertical-align: top;\n", |
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| 6134 | " }\n", |
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| 6136 | " .dataframe thead th {\n", |
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| 6137 | " text-align: right;\n", |
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| 6138 | " }\n", |
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| 6139 | "</style>\n", |
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| 6140 | "<table border=\"1\" class=\"dataframe\">\n", |
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| 6141 | " <thead>\n", |
||
| 6142 | " <tr style=\"text-align: right;\">\n", |
||
| 6143 | " <th></th>\n", |
||
| 6144 | " <th>PATIENT_VISIT_IDENTIFIER</th>\n", |
||
| 6145 | " <th>AGE_ABOVE65</th>\n", |
||
| 6146 | " <th>AGE_PERCENTIL</th>\n", |
||
| 6147 | " <th>GENDER</th>\n", |
||
| 6148 | " <th>DISEASE GROUPING 1</th>\n", |
||
| 6149 | " <th>DISEASE GROUPING 2</th>\n", |
||
| 6150 | " <th>DISEASE GROUPING 3</th>\n", |
||
| 6151 | " <th>DISEASE GROUPING 4</th>\n", |
||
| 6152 | " <th>DISEASE GROUPING 5</th>\n", |
||
| 6153 | " <th>DISEASE GROUPING 6</th>\n", |
||
| 6154 | " <th>...</th>\n", |
||
| 6155 | " <th>OXYGEN_SATURATION_DIFF</th>\n", |
||
| 6156 | " <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n", |
||
| 6157 | " <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n", |
||
| 6158 | " <th>HEART_RATE_DIFF_REL</th>\n", |
||
| 6159 | " <th>RESPIRATORY_RATE_DIFF_REL</th>\n", |
||
| 6160 | " <th>TEMPERATURE_DIFF_REL</th>\n", |
||
| 6161 | " <th>OXYGEN_SATURATION_DIFF_REL</th>\n", |
||
| 6162 | " <th>WINDOW</th>\n", |
||
| 6163 | " <th>ICU</th>\n", |
||
| 6164 | " <th>ICU_SUM</th>\n", |
||
| 6165 | " </tr>\n", |
||
| 6166 | " </thead>\n", |
||
| 6167 | " <tbody>\n", |
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| 6168 | " <tr>\n", |
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| 6169 | " <th>0</th>\n", |
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| 6181 | " <td>-1.000000</td>\n", |
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| 6182 | " <td>-1.000000</td>\n", |
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| 6183 | " <td>-1.000000</td>\n", |
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| 6184 | " <td>-1.000000</td>\n", |
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| 6185 | " <td>-1.000000</td>\n", |
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| 6186 | " <td>-1.000000</td>\n", |
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| 6187 | " <td>-1.000000</td>\n", |
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| 6188 | " <td>0</td>\n", |
||
| 6189 | " <td>0</td>\n", |
||
| 6190 | " <td>1</td>\n", |
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| 6191 | " </tr>\n", |
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| 6192 | " <tr>\n", |
||
| 6193 | " <th>1</th>\n", |
||
| 6194 | " <td>0</td>\n", |
||
| 6195 | " <td>1</td>\n", |
||
| 6196 | " <td>5</td>\n", |
||
| 6197 | " <td>0</td>\n", |
||
| 6198 | " <td>0.0</td>\n", |
||
| 6199 | " <td>0.0</td>\n", |
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| 6200 | " <td>0.0</td>\n", |
||
| 6201 | " <td>0.0</td>\n", |
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| 6202 | " <td>1.0</td>\n", |
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| 6203 | " <td>1.0</td>\n", |
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| 6204 | " <td>...</td>\n", |
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| 6205 | " <td>-1.000000</td>\n", |
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| 6206 | " <td>-1.000000</td>\n", |
||
| 6207 | " <td>-1.000000</td>\n", |
||
| 6208 | " <td>-1.000000</td>\n", |
||
| 6209 | " <td>-1.000000</td>\n", |
||
| 6210 | " <td>-1.000000</td>\n", |
||
| 6211 | " <td>-1.000000</td>\n", |
||
| 6212 | " <td>1</td>\n", |
||
| 6213 | " <td>0</td>\n", |
||
| 6214 | " <td>1</td>\n", |
||
| 6215 | " </tr>\n", |
||
| 6216 | " <tr>\n", |
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| 6217 | " <th>2</th>\n", |
||
| 6218 | " <td>0</td>\n", |
||
| 6219 | " <td>1</td>\n", |
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| 6220 | " <td>5</td>\n", |
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| 6221 | " <td>0</td>\n", |
||
| 6222 | " <td>0.0</td>\n", |
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| 6223 | " <td>0.0</td>\n", |
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| 6224 | " <td>0.0</td>\n", |
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| 6225 | " <td>0.0</td>\n", |
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| 6226 | " <td>1.0</td>\n", |
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| 6227 | " <td>1.0</td>\n", |
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| 6228 | " <td>...</td>\n", |
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| 6229 | " <td>-1.000000</td>\n", |
||
| 6230 | " <td>-1.000000</td>\n", |
||
| 6231 | " <td>-1.000000</td>\n", |
||
| 6232 | " <td>-1.000000</td>\n", |
||
| 6233 | " <td>-1.000000</td>\n", |
||
| 6234 | " <td>-1.000000</td>\n", |
||
| 6235 | " <td>-1.000000</td>\n", |
||
| 6236 | " <td>2</td>\n", |
||
| 6237 | " <td>0</td>\n", |
||
| 6238 | " <td>1</td>\n", |
||
| 6239 | " </tr>\n", |
||
| 6240 | " <tr>\n", |
||
| 6241 | " <th>5</th>\n", |
||
| 6242 | " <td>1</td>\n", |
||
| 6243 | " <td>1</td>\n", |
||
| 6244 | " <td>8</td>\n", |
||
| 6245 | " <td>1</td>\n", |
||
| 6246 | " <td>0.0</td>\n", |
||
| 6247 | " <td>0.0</td>\n", |
||
| 6248 | " <td>0.0</td>\n", |
||
| 6249 | " <td>0.0</td>\n", |
||
| 6250 | " <td>0.0</td>\n", |
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| 6251 | " <td>0.0</td>\n", |
||
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||
| 6253 | " <td>-1.000000</td>\n", |
||
| 6254 | " <td>-1.000000</td>\n", |
||
| 6255 | " <td>-1.000000</td>\n", |
||
| 6256 | " <td>-1.000000</td>\n", |
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| 6257 | " <td>-1.000000</td>\n", |
||
| 6258 | " <td>-1.000000</td>\n", |
||
| 6259 | " <td>-1.000000</td>\n", |
||
| 6260 | " <td>0</td>\n", |
||
| 6261 | " <td>1</td>\n", |
||
| 6262 | " <td>1</td>\n", |
||
| 6263 | " </tr>\n", |
||
| 6264 | " <tr>\n", |
||
| 6265 | " <th>6</th>\n", |
||
| 6266 | " <td>1</td>\n", |
||
| 6267 | " <td>1</td>\n", |
||
| 6268 | " <td>8</td>\n", |
||
| 6269 | " <td>1</td>\n", |
||
| 6270 | " <td>0.0</td>\n", |
||
| 6271 | " <td>0.0</td>\n", |
||
| 6272 | " <td>0.0</td>\n", |
||
| 6273 | " <td>0.0</td>\n", |
||
| 6274 | " <td>0.0</td>\n", |
||
| 6275 | " <td>0.0</td>\n", |
||
| 6276 | " <td>...</td>\n", |
||
| 6277 | " <td>-1.000000</td>\n", |
||
| 6278 | " <td>-1.000000</td>\n", |
||
| 6279 | " <td>-1.000000</td>\n", |
||
| 6280 | " <td>-1.000000</td>\n", |
||
| 6281 | " <td>-1.000000</td>\n", |
||
| 6282 | " <td>-1.000000</td>\n", |
||
| 6283 | " <td>-1.000000</td>\n", |
||
| 6284 | " <td>1</td>\n", |
||
| 6285 | " <td>1</td>\n", |
||
| 6286 | " <td>1</td>\n", |
||
| 6287 | " </tr>\n", |
||
| 6288 | " <tr>\n", |
||
| 6289 | " <th>7</th>\n", |
||
| 6290 | " <td>1</td>\n", |
||
| 6291 | " <td>1</td>\n", |
||
| 6292 | " <td>8</td>\n", |
||
| 6293 | " <td>1</td>\n", |
||
| 6294 | " <td>0.0</td>\n", |
||
| 6295 | " <td>0.0</td>\n", |
||
| 6296 | " <td>0.0</td>\n", |
||
| 6297 | " <td>0.0</td>\n", |
||
| 6298 | " <td>0.0</td>\n", |
||
| 6299 | " <td>0.0</td>\n", |
||
| 6300 | " <td>...</td>\n", |
||
| 6301 | " <td>-1.000000</td>\n", |
||
| 6302 | " <td>-1.000000</td>\n", |
||
| 6303 | " <td>-1.000000</td>\n", |
||
| 6304 | " <td>-1.000000</td>\n", |
||
| 6305 | " <td>-1.000000</td>\n", |
||
| 6306 | " <td>-1.000000</td>\n", |
||
| 6307 | " <td>-1.000000</td>\n", |
||
| 6308 | " <td>2</td>\n", |
||
| 6309 | " <td>1</td>\n", |
||
| 6310 | " <td>1</td>\n", |
||
| 6311 | " </tr>\n", |
||
| 6312 | " <tr>\n", |
||
| 6313 | " <th>10</th>\n", |
||
| 6314 | " <td>2</td>\n", |
||
| 6315 | " <td>0</td>\n", |
||
| 6316 | " <td>0</td>\n", |
||
| 6317 | " <td>0</td>\n", |
||
| 6318 | " <td>0.0</td>\n", |
||
| 6319 | " <td>0.0</td>\n", |
||
| 6320 | " <td>0.0</td>\n", |
||
| 6321 | " <td>0.0</td>\n", |
||
| 6322 | " <td>0.0</td>\n", |
||
| 6323 | " <td>0.0</td>\n", |
||
| 6324 | " <td>...</td>\n", |
||
| 6325 | " <td>-0.959596</td>\n", |
||
| 6326 | " <td>-0.515528</td>\n", |
||
| 6327 | " <td>-0.351328</td>\n", |
||
| 6328 | " <td>-0.747001</td>\n", |
||
| 6329 | " <td>-0.756272</td>\n", |
||
| 6330 | " <td>-1.000000</td>\n", |
||
| 6331 | " <td>-0.961262</td>\n", |
||
| 6332 | " <td>0</td>\n", |
||
| 6333 | " <td>0</td>\n", |
||
| 6334 | " <td>1</td>\n", |
||
| 6335 | " </tr>\n", |
||
| 6336 | " <tr>\n", |
||
| 6337 | " <th>11</th>\n", |
||
| 6338 | " <td>2</td>\n", |
||
| 6339 | " <td>0</td>\n", |
||
| 6340 | " <td>0</td>\n", |
||
| 6341 | " <td>0</td>\n", |
||
| 6342 | " <td>0.0</td>\n", |
||
| 6343 | " <td>0.0</td>\n", |
||
| 6344 | " <td>0.0</td>\n", |
||
| 6345 | " <td>0.0</td>\n", |
||
| 6346 | " <td>0.0</td>\n", |
||
| 6347 | " <td>0.0</td>\n", |
||
| 6348 | " <td>...</td>\n", |
||
| 6349 | " <td>-0.959596</td>\n", |
||
| 6350 | " <td>-0.515528</td>\n", |
||
| 6351 | " <td>-0.351328</td>\n", |
||
| 6352 | " <td>-0.747001</td>\n", |
||
| 6353 | " <td>-0.756272</td>\n", |
||
| 6354 | " <td>-1.000000</td>\n", |
||
| 6355 | " <td>-0.961262</td>\n", |
||
| 6356 | " <td>1</td>\n", |
||
| 6357 | " <td>0</td>\n", |
||
| 6358 | " <td>1</td>\n", |
||
| 6359 | " </tr>\n", |
||
| 6360 | " <tr>\n", |
||
| 6361 | " <th>12</th>\n", |
||
| 6362 | " <td>2</td>\n", |
||
| 6363 | " <td>0</td>\n", |
||
| 6364 | " <td>0</td>\n", |
||
| 6365 | " <td>0</td>\n", |
||
| 6366 | " <td>0.0</td>\n", |
||
| 6367 | " <td>0.0</td>\n", |
||
| 6368 | " <td>0.0</td>\n", |
||
| 6369 | " <td>0.0</td>\n", |
||
| 6370 | " <td>0.0</td>\n", |
||
| 6371 | " <td>0.0</td>\n", |
||
| 6372 | " <td>...</td>\n", |
||
| 6373 | " <td>-0.959596</td>\n", |
||
| 6374 | " <td>-0.515528</td>\n", |
||
| 6375 | " <td>-0.351328</td>\n", |
||
| 6376 | " <td>-0.747001</td>\n", |
||
| 6377 | " <td>-0.756272</td>\n", |
||
| 6378 | " <td>-1.000000</td>\n", |
||
| 6379 | " <td>-0.961262</td>\n", |
||
| 6380 | " <td>2</td>\n", |
||
| 6381 | " <td>0</td>\n", |
||
| 6382 | " <td>1</td>\n", |
||
| 6383 | " </tr>\n", |
||
| 6384 | " <tr>\n", |
||
| 6385 | " <th>15</th>\n", |
||
| 6386 | " <td>3</td>\n", |
||
| 6387 | " <td>0</td>\n", |
||
| 6388 | " <td>3</td>\n", |
||
| 6389 | " <td>1</td>\n", |
||
| 6390 | " <td>0.0</td>\n", |
||
| 6391 | " <td>0.0</td>\n", |
||
| 6392 | " <td>0.0</td>\n", |
||
| 6393 | " <td>0.0</td>\n", |
||
| 6394 | " <td>0.0</td>\n", |
||
| 6395 | " <td>0.0</td>\n", |
||
| 6396 | " <td>...</td>\n", |
||
| 6397 | " <td>-1.000000</td>\n", |
||
| 6398 | " <td>-1.000000</td>\n", |
||
| 6399 | " <td>-1.000000</td>\n", |
||
| 6400 | " <td>-1.000000</td>\n", |
||
| 6401 | " <td>-1.000000</td>\n", |
||
| 6402 | " <td>-1.000000</td>\n", |
||
| 6403 | " <td>-1.000000</td>\n", |
||
| 6404 | " <td>0</td>\n", |
||
| 6405 | " <td>0</td>\n", |
||
| 6406 | " <td>0</td>\n", |
||
| 6407 | " </tr>\n", |
||
| 6408 | " <tr>\n", |
||
| 6409 | " <th>16</th>\n", |
||
| 6410 | " <td>3</td>\n", |
||
| 6411 | " <td>0</td>\n", |
||
| 6412 | " <td>3</td>\n", |
||
| 6413 | " <td>1</td>\n", |
||
| 6414 | " <td>0.0</td>\n", |
||
| 6415 | " <td>0.0</td>\n", |
||
| 6416 | " <td>0.0</td>\n", |
||
| 6417 | " <td>0.0</td>\n", |
||
| 6418 | " <td>0.0</td>\n", |
||
| 6419 | " <td>0.0</td>\n", |
||
| 6420 | " <td>...</td>\n", |
||
| 6421 | " <td>-1.000000</td>\n", |
||
| 6422 | " <td>-1.000000</td>\n", |
||
| 6423 | " <td>-1.000000</td>\n", |
||
| 6424 | " <td>-1.000000</td>\n", |
||
| 6425 | " <td>-1.000000</td>\n", |
||
| 6426 | " <td>-1.000000</td>\n", |
||
| 6427 | " <td>-1.000000</td>\n", |
||
| 6428 | " <td>1</td>\n", |
||
| 6429 | " <td>0</td>\n", |
||
| 6430 | " <td>0</td>\n", |
||
| 6431 | " </tr>\n", |
||
| 6432 | " <tr>\n", |
||
| 6433 | " <th>17</th>\n", |
||
| 6434 | " <td>3</td>\n", |
||
| 6435 | " <td>0</td>\n", |
||
| 6436 | " <td>3</td>\n", |
||
| 6437 | " <td>1</td>\n", |
||
| 6438 | " <td>0.0</td>\n", |
||
| 6439 | " <td>0.0</td>\n", |
||
| 6440 | " <td>0.0</td>\n", |
||
| 6441 | " <td>0.0</td>\n", |
||
| 6442 | " <td>0.0</td>\n", |
||
| 6443 | " <td>0.0</td>\n", |
||
| 6444 | " <td>...</td>\n", |
||
| 6445 | " <td>-1.000000</td>\n", |
||
| 6446 | " <td>-1.000000</td>\n", |
||
| 6447 | " <td>-1.000000</td>\n", |
||
| 6448 | " <td>-1.000000</td>\n", |
||
| 6449 | " <td>-1.000000</td>\n", |
||
| 6450 | " <td>-1.000000</td>\n", |
||
| 6451 | " <td>-1.000000</td>\n", |
||
| 6452 | " <td>2</td>\n", |
||
| 6453 | " <td>0</td>\n", |
||
| 6454 | " <td>0</td>\n", |
||
| 6455 | " </tr>\n", |
||
| 6456 | " <tr>\n", |
||
| 6457 | " <th>19</th>\n", |
||
| 6458 | " <td>3</td>\n", |
||
| 6459 | " <td>0</td>\n", |
||
| 6460 | " <td>3</td>\n", |
||
| 6461 | " <td>1</td>\n", |
||
| 6462 | " <td>0.0</td>\n", |
||
| 6463 | " <td>0.0</td>\n", |
||
| 6464 | " <td>0.0</td>\n", |
||
| 6465 | " <td>0.0</td>\n", |
||
| 6466 | " <td>0.0</td>\n", |
||
| 6467 | " <td>0.0</td>\n", |
||
| 6468 | " <td>...</td>\n", |
||
| 6469 | " <td>-0.171717</td>\n", |
||
| 6470 | " <td>-0.308696</td>\n", |
||
| 6471 | " <td>-0.057718</td>\n", |
||
| 6472 | " <td>-0.069094</td>\n", |
||
| 6473 | " <td>-0.329749</td>\n", |
||
| 6474 | " <td>-0.047619</td>\n", |
||
| 6475 | " <td>-0.172436</td>\n", |
||
| 6476 | " <td>4</td>\n", |
||
| 6477 | " <td>0</td>\n", |
||
| 6478 | " <td>0</td>\n", |
||
| 6479 | " </tr>\n", |
||
| 6480 | " <tr>\n", |
||
| 6481 | " <th>20</th>\n", |
||
| 6482 | " <td>4</td>\n", |
||
| 6483 | " <td>0</td>\n", |
||
| 6484 | " <td>0</td>\n", |
||
| 6485 | " <td>0</td>\n", |
||
| 6486 | " <td>0.0</td>\n", |
||
| 6487 | " <td>0.0</td>\n", |
||
| 6488 | " <td>0.0</td>\n", |
||
| 6489 | " <td>0.0</td>\n", |
||
| 6490 | " <td>0.0</td>\n", |
||
| 6491 | " <td>0.0</td>\n", |
||
| 6492 | " <td>...</td>\n", |
||
| 6493 | " <td>-0.979798</td>\n", |
||
| 6494 | " <td>-1.000000</td>\n", |
||
| 6495 | " <td>-0.883669</td>\n", |
||
| 6496 | " <td>-0.956805</td>\n", |
||
| 6497 | " <td>-0.870968</td>\n", |
||
| 6498 | " <td>-0.953536</td>\n", |
||
| 6499 | " <td>-0.980333</td>\n", |
||
| 6500 | " <td>0</td>\n", |
||
| 6501 | " <td>0</td>\n", |
||
| 6502 | " <td>0</td>\n", |
||
| 6503 | " </tr>\n", |
||
| 6504 | " <tr>\n", |
||
| 6505 | " <th>21</th>\n", |
||
| 6506 | " <td>4</td>\n", |
||
| 6507 | " <td>0</td>\n", |
||
| 6508 | " <td>0</td>\n", |
||
| 6509 | " <td>0</td>\n", |
||
| 6510 | " <td>0.0</td>\n", |
||
| 6511 | " <td>0.0</td>\n", |
||
| 6512 | " <td>0.0</td>\n", |
||
| 6513 | " <td>0.0</td>\n", |
||
| 6514 | " <td>0.0</td>\n", |
||
| 6515 | " <td>0.0</td>\n", |
||
| 6516 | " <td>...</td>\n", |
||
| 6517 | " <td>-0.979798</td>\n", |
||
| 6518 | " <td>-1.000000</td>\n", |
||
| 6519 | " <td>-0.883669</td>\n", |
||
| 6520 | " <td>-0.956805</td>\n", |
||
| 6521 | " <td>-0.870968</td>\n", |
||
| 6522 | " <td>-0.953536</td>\n", |
||
| 6523 | " <td>-0.980333</td>\n", |
||
| 6524 | " <td>1</td>\n", |
||
| 6525 | " <td>0</td>\n", |
||
| 6526 | " <td>0</td>\n", |
||
| 6527 | " </tr>\n", |
||
| 6528 | " </tbody>\n", |
||
| 6529 | "</table>\n", |
||
| 6530 | "<p>15 rows × 232 columns</p>\n", |
||
| 6531 | "</div>\n", |
||
| 6532 | " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4344241e-c0bb-4623-a19f-46417310e52a')\"\n", |
||
| 6533 | " title=\"Convert this dataframe to an interactive table.\"\n", |
||
| 6534 | " style=\"display:none;\">\n", |
||
| 6535 | " \n", |
||
| 6536 | " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", |
||
| 6537 | " width=\"24px\">\n", |
||
| 6538 | " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", |
||
| 6539 | " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", |
||
| 6540 | " </svg>\n", |
||
| 6541 | " </button>\n", |
||
| 6542 | " \n", |
||
| 6543 | " <style>\n", |
||
| 6544 | " .colab-df-container {\n", |
||
| 6545 | " display:flex;\n", |
||
| 6546 | " flex-wrap:wrap;\n", |
||
| 6547 | " gap: 12px;\n", |
||
| 6548 | " }\n", |
||
| 6549 | "\n", |
||
| 6550 | " .colab-df-convert {\n", |
||
| 6551 | " background-color: #E8F0FE;\n", |
||
| 6552 | " border: none;\n", |
||
| 6553 | " border-radius: 50%;\n", |
||
| 6554 | " cursor: pointer;\n", |
||
| 6555 | " display: none;\n", |
||
| 6556 | " fill: #1967D2;\n", |
||
| 6557 | " height: 32px;\n", |
||
| 6558 | " padding: 0 0 0 0;\n", |
||
| 6559 | " width: 32px;\n", |
||
| 6560 | " }\n", |
||
| 6561 | "\n", |
||
| 6562 | " .colab-df-convert:hover {\n", |
||
| 6563 | " background-color: #E2EBFA;\n", |
||
| 6564 | " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", |
||
| 6565 | " fill: #174EA6;\n", |
||
| 6566 | " }\n", |
||
| 6567 | "\n", |
||
| 6568 | " [theme=dark] .colab-df-convert {\n", |
||
| 6569 | " background-color: #3B4455;\n", |
||
| 6570 | " fill: #D2E3FC;\n", |
||
| 6571 | " }\n", |
||
| 6572 | "\n", |
||
| 6573 | " [theme=dark] .colab-df-convert:hover {\n", |
||
| 6574 | " background-color: #434B5C;\n", |
||
| 6575 | " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", |
||
| 6576 | " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", |
||
| 6577 | " fill: #FFFFFF;\n", |
||
| 6578 | " }\n", |
||
| 6579 | " </style>\n", |
||
| 6580 | "\n", |
||
| 6581 | " <script>\n", |
||
| 6582 | " const buttonEl =\n", |
||
| 6583 | " document.querySelector('#df-4344241e-c0bb-4623-a19f-46417310e52a button.colab-df-convert');\n", |
||
| 6584 | " buttonEl.style.display =\n", |
||
| 6585 | " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", |
||
| 6586 | "\n", |
||
| 6587 | " async function convertToInteractive(key) {\n", |
||
| 6588 | " const element = document.querySelector('#df-4344241e-c0bb-4623-a19f-46417310e52a');\n", |
||
| 6589 | " const dataTable =\n", |
||
| 6590 | " await google.colab.kernel.invokeFunction('convertToInteractive',\n", |
||
| 6591 | " [key], {});\n", |
||
| 6592 | " if (!dataTable) return;\n", |
||
| 6593 | "\n", |
||
| 6594 | " const docLinkHtml = 'Like what you see? Visit the ' +\n", |
||
| 6595 | " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", |
||
| 6596 | " + ' to learn more about interactive tables.';\n", |
||
| 6597 | " element.innerHTML = '';\n", |
||
| 6598 | " dataTable['output_type'] = 'display_data';\n", |
||
| 6599 | " await google.colab.output.renderOutput(dataTable, element);\n", |
||
| 6600 | " const docLink = document.createElement('div');\n", |
||
| 6601 | " docLink.innerHTML = docLinkHtml;\n", |
||
| 6602 | " element.appendChild(docLink);\n", |
||
| 6603 | " }\n", |
||
| 6604 | " </script>\n", |
||
| 6605 | " </div>\n", |
||
| 6606 | " </div>\n", |
||
| 6607 | " " |
||
| 6608 | ] |
||
| 6609 | }, |
||
| 6610 | "metadata": {}, |
||
| 6611 | "execution_count": 42 |
||
| 6612 | } |
||
| 6613 | ] |
||
| 6614 | }, |
||
| 6615 | { |
||
| 6616 | "cell_type": "code", |
||
| 6617 | "source": [ |
||
| 6618 | "score_remove_window_34 = log_reg_model(model_data)" |
||
| 6619 | ], |
||
| 6620 | "metadata": { |
||
| 6621 | "id": "LInC8YTwGKvB", |
||
| 6622 | "colab": { |
||
| 6623 | "base_uri": "https://localhost:8080/", |
||
| 6624 | "height": 729 |
||
| 6625 | }, |
||
| 6626 | "outputId": "ac04845a-fc17-4e70-b135-5bacc7aef7ec" |
||
| 6627 | }, |
||
| 6628 | "execution_count": 43, |
||
| 6629 | "outputs": [ |
||
| 6630 | { |
||
| 6631 | "output_type": "stream", |
||
| 6632 | "name": "stdout", |
||
| 6633 | "text": [ |
||
| 6634 | "Training data: (1347, 231)\n", |
||
| 6635 | "Test data: (578, 231)\n", |
||
| 6636 | "\n" |
||
| 6637 | ] |
||
| 6638 | }, |
||
| 6639 | { |
||
| 6640 | "output_type": "stream", |
||
| 6641 | "name": "stderr", |
||
| 6642 | "text": [ |
||
| 6643 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", |
||
| 6644 | " y = column_or_1d(y, warn=True)\n" |
||
| 6645 | ] |
||
| 6646 | }, |
||
| 6647 | { |
||
| 6648 | "output_type": "stream", |
||
| 6649 | "name": "stdout", |
||
| 6650 | "text": [ |
||
| 6651 | " precision recall f1-score support\n", |
||
| 6652 | "\n", |
||
| 6653 | " 0 0.93 0.95 0.94 423\n", |
||
| 6654 | " 1 0.86 0.80 0.83 155\n", |
||
| 6655 | "\n", |
||
| 6656 | " accuracy 0.91 578\n", |
||
| 6657 | " macro avg 0.89 0.88 0.88 578\n", |
||
| 6658 | "weighted avg 0.91 0.91 0.91 578\n", |
||
| 6659 | "\n", |
||
| 6660 | "ROC_AUC_Score: 0.875177304964539\n" |
||
| 6661 | ] |
||
| 6662 | }, |
||
| 6663 | { |
||
| 6664 | "output_type": "display_data", |
||
| 6665 | "data": { |
||
| 6666 | "text/plain": [ |
||
| 6667 | "<Figure size 700x500 with 2 Axes>" |
||
| 6668 | ], |
||
| 6669 | "image/png": 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\n" |
||
| 6670 | }, |
||
| 6671 | "metadata": {} |
||
| 6672 | } |
||
| 6673 | ] |
||
| 6674 | }, |
||
| 6675 | { |
||
| 6676 | "cell_type": "code", |
||
| 6677 | "source": [ |
||
| 6678 | "score_remove_window_34 = decision_tree_model(model_data)" |
||
| 6679 | ], |
||
| 6680 | "metadata": { |
||
| 6681 | "id": "7MLKLbf7GSPS", |
||
| 6682 | "colab": { |
||
| 6683 | "base_uri": "https://localhost:8080/", |
||
| 6684 | "height": 671 |
||
| 6685 | }, |
||
| 6686 | "outputId": "d46c3d0a-2eb8-4b72-894e-0819061cf78e" |
||
| 6687 | }, |
||
| 6688 | "execution_count": 44, |
||
| 6689 | "outputs": [ |
||
| 6690 | { |
||
| 6691 | "output_type": "stream", |
||
| 6692 | "name": "stdout", |
||
| 6693 | "text": [ |
||
| 6694 | "Training data: (1347, 231)\n", |
||
| 6695 | "Test data: (578, 231)\n", |
||
| 6696 | "\n", |
||
| 6697 | " precision recall f1-score support\n", |
||
| 6698 | "\n", |
||
| 6699 | " 0 0.92 0.93 0.93 423\n", |
||
| 6700 | " 1 0.81 0.78 0.80 155\n", |
||
| 6701 | "\n", |
||
| 6702 | " accuracy 0.89 578\n", |
||
| 6703 | " macro avg 0.87 0.86 0.86 578\n", |
||
| 6704 | "weighted avg 0.89 0.89 0.89 578\n", |
||
| 6705 | "\n", |
||
| 6706 | "ROC_AUC_Score: 0.8572256539312134\n" |
||
| 6707 | ] |
||
| 6708 | }, |
||
| 6709 | { |
||
| 6710 | "output_type": "display_data", |
||
| 6711 | "data": { |
||
| 6712 | "text/plain": [ |
||
| 6713 | "<Figure size 700x500 with 2 Axes>" |
||
| 6714 | ], |
||
| 6715 | "image/png": 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\n" |
||
| 6716 | }, |
||
| 6717 | "metadata": {} |
||
| 6718 | } |
||
| 6719 | ] |
||
| 6720 | }, |
||
| 6721 | { |
||
| 6722 | "cell_type": "code", |
||
| 6723 | "source": [ |
||
| 6724 | "score_remove_window_34 = k_neighbors_model(model_data)" |
||
| 6725 | ], |
||
| 6726 | "metadata": { |
||
| 6727 | "id": "5JRBg_nEGXse", |
||
| 6728 | "colab": { |
||
| 6729 | "base_uri": "https://localhost:8080/", |
||
| 6730 | "height": 725 |
||
| 6731 | }, |
||
| 6732 | "outputId": "d5caa0af-550b-4d95-bf6b-e8672435b1c3" |
||
| 6733 | }, |
||
| 6734 | "execution_count": 45, |
||
| 6735 | "outputs": [ |
||
| 6736 | { |
||
| 6737 | "output_type": "stream", |
||
| 6738 | "name": "stdout", |
||
| 6739 | "text": [ |
||
| 6740 | "Training data: (1347, 231)\n", |
||
| 6741 | "Test data: (578, 231)\n", |
||
| 6742 | "\n", |
||
| 6743 | " precision recall f1-score support\n", |
||
| 6744 | "\n", |
||
| 6745 | " 0 0.78 1.00 0.87 423\n", |
||
| 6746 | " 1 0.97 0.21 0.35 155\n", |
||
| 6747 | "\n", |
||
| 6748 | " accuracy 0.79 578\n", |
||
| 6749 | " macro avg 0.87 0.61 0.61 578\n", |
||
| 6750 | "weighted avg 0.83 0.79 0.73 578\n", |
||
| 6751 | "\n", |
||
| 6752 | "ROC_AUC_Score: 0.6052695798062991\n" |
||
| 6753 | ] |
||
| 6754 | }, |
||
| 6755 | { |
||
| 6756 | "output_type": "stream", |
||
| 6757 | "name": "stderr", |
||
| 6758 | "text": [ |
||
| 6759 | "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", |
||
| 6760 | " return self._fit(X, y)\n" |
||
| 6761 | ] |
||
| 6762 | }, |
||
| 6763 | { |
||
| 6764 | "output_type": "display_data", |
||
| 6765 | "data": { |
||
| 6766 | "text/plain": [ |
||
| 6767 | "<Figure size 700x500 with 2 Axes>" |
||
| 6768 | ], |
||
| 6769 | "image/png": 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\n" |
||
| 6770 | }, |
||
| 6771 | "metadata": {} |
||
| 6772 | } |
||
| 6773 | ] |
||
| 6774 | }, |
||
| 6775 | { |
||
| 6776 | "cell_type": "markdown", |
||
| 6777 | "source": [ |
||
| 6778 | "Logistic Regression and Decision Tree models consistently show correct predictions and efficiency for ICU admissions at around 85-90% with limited false positive and false negative results." |
||
| 6779 | ], |
||
| 6780 | "metadata": { |
||
| 6781 | "id": "gZmFUo3kFrgv" |
||
| 6782 | } |
||
| 6783 | }, |
||
| 6784 | { |
||
| 6785 | "cell_type": "markdown", |
||
| 6786 | "source": [ |
||
| 6787 | "**Conclusion**\n", |
||
| 6788 | "\n", |
||
| 6789 | "In conclusion, logistic regression and decision tree algorithms can be used to predict the need for confirmed COVID-19 patients to be admitted into ICU given clinical data. Our healthcare systems must incorporate innovative use of technologies like machine learning to improve the overall efficiency of hospitals and use of the intensive care unit. My machine learning models can provide solutions and assist doctors in critical decisions for saving lives." |
||
| 6790 | ], |
||
| 6791 | "metadata": { |
||
| 6792 | "id": "FlapQZFfwuZk" |
||
| 6793 | } |
||
| 6794 | } |
||
| 6795 | ] |
||
| 6796 | } |