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Clinical Deterioration Prediction Model - Preprocessing I.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clinical Deterioration Prediction Model - Preprocessing I\n",
"\n",
"\n",
"## Extract data from MIMIC III Datasets and Organize by Patient. \n",
"\n",
"## Data Source \n",
"\n",
"For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain acess to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data acess request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907\n",
"\n",
"The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. \n",
"\n",
"Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import sys\n",
"import csv"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\Users\\\\abebu\\\\Google Drive\\\\mimic-iii-clinical-database-1.4'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"os.chdir(\"C://Users/abebu/Google Drive/mimic-iii-clinical-database-1.4\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 46520 entries, 234 to 31844\n",
"Data columns (total 7 columns):\n",
"SUBJECT_ID 46520 non-null int64\n",
"GENDER 46520 non-null object\n",
"DOB 46520 non-null object\n",
"DOD 15759 non-null object\n",
"DOD_HOSP 9974 non-null object\n",
"DOD_SSN 13378 non-null object\n",
"EXPIRE_FLAG 46520 non-null int64\n",
"dtypes: int64(2), object(5)\n",
"memory usage: 2.8+ MB\n"
]
}
],
"source": [
"pt=pd.read_csv('PATIENTS.csv', header=0, index_col=0)\n",
"pt.info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"demo=pt[['SUBJECT_ID', 'GENDER']]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
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" <th>GENDER</th>\n",
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" <th>DOD</th>\n",
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" <td>234</td>\n",
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" <td>2075-03-13</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" SUBJECT_ID GENDER DOB DOD DOD_HOSP DOD_SSN \\\n",
"ROW_ID \n",
"234 249 F 2075-03-13 NaT NaN NaN \n",
"235 250 F 2164-12-27 2188-11-22 2188-11-22 00:00:00 NaN \n",
"236 251 M 2090-03-15 NaT NaN NaN \n",
"237 252 M 2078-03-06 NaT NaN NaN \n",
"238 253 F 2089-11-26 NaT NaN NaN \n",
"\n",
" EXPIRE_FLAG \n",
"ROW_ID \n",
"234 0 \n",
"235 1 \n",
"236 0 \n",
"237 0 \n",
"238 0 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pt.DOB = pd.to_datetime(pt.DOB)\n",
"pt.DOD = pd.to_datetime(pt.DOD)\n",
"pt.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 58976 entries, 21 to 58598\n",
"Data columns (total 18 columns):\n",
"SUBJECT_ID 58976 non-null int64\n",
"HADM_ID 58976 non-null int64\n",
"ADMITTIME 58976 non-null object\n",
"DISCHTIME 58976 non-null object\n",
"DEATHTIME 5854 non-null object\n",
"ADMISSION_TYPE 58976 non-null object\n",
"ADMISSION_LOCATION 58976 non-null object\n",
"DISCHARGE_LOCATION 58976 non-null object\n",
"INSURANCE 58976 non-null object\n",
"LANGUAGE 33644 non-null object\n",
"RELIGION 58518 non-null object\n",
"MARITAL_STATUS 48848 non-null object\n",
"ETHNICITY 58976 non-null object\n",
"EDREGTIME 30877 non-null object\n",
"EDOUTTIME 30877 non-null object\n",
"DIAGNOSIS 58951 non-null object\n",
"HOSPITAL_EXPIRE_FLAG 58976 non-null int64\n",
"HAS_CHARTEVENTS_DATA 58976 non-null int64\n",
"dtypes: int64(4), object(14)\n",
"memory usage: 8.5+ MB\n"
]
}
],
"source": [
"adm=pd.read_csv(\"ADMISSIONS.csv\", header=0, index_col=0) \n",
"adm.info()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"demo1 = adm[['SUBJECT_ID', 'HADM_ID', 'INSURANCE', 'RELIGION', 'MARITAL_STATUS', 'ETHNICITY']]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
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" <td>NaT</td>\n",
" <td>EMERGENCY</td>\n",
" <td>0</td>\n",
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" <td>22</td>\n",
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" <td>NaT</td>\n",
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" <td>NaT</td>\n",
" <td>EMERGENCY</td>\n",
" <td>0</td>\n",
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"text/plain": [
" SUBJECT_ID HADM_ID ADMITTIME DISCHTIME DEATHTIME \\\n",
"ROW_ID \n",
"21 22 165315 2196-04-09 12:26:00 2196-04-10 15:54:00 NaT \n",
"22 23 152223 2153-09-03 07:15:00 2153-09-08 19:10:00 NaT \n",
"23 23 124321 2157-10-18 19:34:00 2157-10-25 14:00:00 NaT \n",
"24 24 161859 2139-06-06 16:14:00 2139-06-09 12:48:00 NaT \n",
"25 25 129635 2160-11-02 02:06:00 2160-11-05 14:55:00 NaT \n",
"\n",
" ADMISSION_TYPE HOSPITAL_EXPIRE_FLAG \n",
"ROW_ID \n",
"21 EMERGENCY 0 \n",
"22 ELECTIVE 0 \n",
"23 EMERGENCY 0 \n",
"24 EMERGENCY 0 \n",
"25 EMERGENCY 0 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adm = adm[['SUBJECT_ID', 'HADM_ID', 'ADMITTIME', 'DISCHTIME', 'DEATHTIME', 'ADMISSION_TYPE', 'HOSPITAL_EXPIRE_FLAG']]\n",
"adm.ADMITTIME = pd.to_datetime(adm.ADMITTIME)\n",
"adm.DISCHTIME = pd.to_datetime(adm.DISCHTIME)\n",
"adm.DEATHTIME = pd.to_datetime(adm.DEATHTIME)\n",
"adm.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 61532 entries, 365 to 59810\n",
"Data columns (total 11 columns):\n",
"SUBJECT_ID 61532 non-null int64\n",
"HADM_ID 61532 non-null int64\n",
"ICUSTAY_ID 61532 non-null int64\n",
"DBSOURCE 61532 non-null object\n",
"FIRST_CAREUNIT 61532 non-null object\n",
"LAST_CAREUNIT 61532 non-null object\n",
"FIRST_WARDID 61532 non-null int64\n",
"LAST_WARDID 61532 non-null int64\n",
"INTIME 61532 non-null object\n",
"OUTTIME 61522 non-null object\n",
"LOS 61522 non-null float64\n",
"dtypes: float64(1), int64(5), object(5)\n",
"memory usage: 5.6+ MB\n"
]
}
],
"source": [
"icu=pd.read_csv(\"ICUSTAYS.csv\", header=0, index_col=0) \n",
"icu.info()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"icu.INTIME = pd.to_datetime(icu.INTIME)\n",
"icu.OUTTIME = pd.to_datetime(icu.OUTTIME)\n",
"icu.ICUSTAY_ID.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"icu=icu[['SUBJECT_ID', 'HADM_ID', 'ICUSTAY_ID', 'INTIME', 'OUTTIME', 'LOS']]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def subj_merge(table1, table2):\n",
" return table1.merge(table2, how='inner', left_on=['SUBJECT_ID'], right_on=['SUBJECT_ID'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 58976 entries, 0 to 58975\n",
"Data columns (total 13 columns):\n",
"SUBJECT_ID 58976 non-null int64\n",
"HADM_ID 58976 non-null int64\n",
"ADMITTIME 58976 non-null datetime64[ns]\n",
"DISCHTIME 58976 non-null datetime64[ns]\n",
"DEATHTIME 5854 non-null datetime64[ns]\n",
"ADMISSION_TYPE 58976 non-null object\n",
"HOSPITAL_EXPIRE_FLAG 58976 non-null int64\n",
"GENDER 58976 non-null object\n",
"DOB 58976 non-null datetime64[ns]\n",
"DOD 22586 non-null datetime64[ns]\n",
"DOD_HOSP 15071 non-null object\n",
"DOD_SSN 19069 non-null object\n",
"EXPIRE_FLAG 58976 non-null int64\n",
"dtypes: datetime64[ns](5), int64(4), object(4)\n",
"memory usage: 6.3+ MB\n"
]
}
],
"source": [
"adm=subj_merge(adm, pt)\n",
"adm.info()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"adm=adm.drop(['DEATHTIME', 'DOD_SSN'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
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" <td>58973</td>\n",
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" <td>MARRIED</td>\n",
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"<p>58976 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID GENDER HADM_ID INSURANCE RELIGION MARITAL_STATUS \\\n",
"0 249 F 116935 Medicare CATHOLIC DIVORCED \n",
"1 249 F 149546 Medicare CATHOLIC DIVORCED \n",
"2 249 F 158975 Medicare CATHOLIC DIVORCED \n",
"3 250 F 124271 Self Pay NOT SPECIFIED SINGLE \n",
"4 251 M 117937 Private OTHER NaN \n",
"... ... ... ... ... ... ... \n",
"58971 44089 M 165748 Medicare GREEK ORTHODOX MARRIED \n",
"58972 44115 F 163623 Private CATHOLIC MARRIED \n",
"58973 44123 F 116395 Medicare CATHOLIC SEPARATED \n",
"58974 44126 F 183530 Private NOT SPECIFIED MARRIED \n",
"58975 44128 M 141304 Private NOT SPECIFIED MARRIED \n",
"\n",
" ETHNICITY \n",
"0 WHITE \n",
"1 WHITE \n",
"2 WHITE \n",
"3 BLACK/AFRICAN AMERICAN \n",
"4 UNKNOWN/NOT SPECIFIED \n",
"... ... \n",
"58971 WHITE \n",
"58972 WHITE \n",
"58973 WHITE \n",
"58974 WHITE \n",
"58975 WHITE \n",
"\n",
"[58976 rows x 7 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo=subj_merge(demo, demo1)\n",
"demo"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"demo.to_csv('demography.csv')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def adm_merge(table1, table2):\n",
" return table1.merge(table2, how='left', left_on=['SUBJECT_ID','HADM_ID'], right_on=['SUBJECT_ID','HADM_ID'])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"icu=adm_merge(icu, adm)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
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"... ... ... ... ... \n",
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},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"icu"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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" <td>2183-03-25 09:53:10</td>\n",
" <td>2183-03-27 17:55:03</td>\n",
" <td>2.3346</td>\n",
" <td>2183-03-25</td>\n",
" <td>2183-04-01 17:07:00</td>\n",
" <td>ELECTIVE</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" <td>2115-05-23</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>67.884932</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61531</td>\n",
" <td>94956</td>\n",
" <td>156386</td>\n",
" <td>275346</td>\n",
" <td>2157-05-19 02:54:54</td>\n",
" <td>2157-05-23 14:58:04</td>\n",
" <td>4.5022</td>\n",
" <td>2157-05-19</td>\n",
" <td>2157-05-25 16:40:00</td>\n",
" <td>EMERGENCY</td>\n",
" <td>0</td>\n",
" <td>M</td>\n",
" <td>2123-03-01</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>34.241096</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>61532 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID INTIME \\\n",
"0 268 110404 280836 2198-02-14 23:27:38 \n",
"1 269 106296 206613 2170-11-05 11:05:29 \n",
"2 270 188028 220345 2128-06-24 15:05:20 \n",
"3 271 173727 249196 2120-08-07 23:12:42 \n",
"4 272 164716 210407 2186-12-25 21:08:04 \n",
"... ... ... ... ... \n",
"61527 94944 143774 201233 2104-04-15 10:18:16 \n",
"61528 94950 123750 283653 2155-12-08 05:33:16 \n",
"61529 94953 196881 241585 2160-03-03 16:09:11 \n",
"61530 94954 118475 202802 2183-03-25 09:53:10 \n",
"61531 94956 156386 275346 2157-05-19 02:54:54 \n",
"\n",
" OUTTIME LOS ADMITTIME DISCHTIME \\\n",
"0 2198-02-18 05:26:11 3.2490 2198-02-11 2198-02-18 03:55:00 \n",
"1 2170-11-08 17:46:57 3.2788 2170-11-05 2170-11-27 18:00:00 \n",
"2 2128-06-27 12:32:29 2.8939 2128-06-23 2128-06-27 12:31:00 \n",
"3 2120-08-10 00:39:04 2.0600 2120-08-07 2120-08-20 16:00:00 \n",
"4 2186-12-27 12:01:13 1.6202 2186-12-25 2187-01-02 14:57:00 \n",
"... ... ... ... ... \n",
"61527 2104-04-17 14:51:00 2.1894 2104-04-11 2104-04-20 16:16:00 \n",
"61528 2155-12-10 17:24:58 2.4942 2155-12-07 2155-12-12 10:10:00 \n",
"61529 2160-03-04 14:22:33 0.9259 2160-03-03 2160-03-04 12:48:00 \n",
"61530 2183-03-27 17:55:03 2.3346 2183-03-25 2183-04-01 17:07:00 \n",
"61531 2157-05-23 14:58:04 4.5022 2157-05-19 2157-05-25 16:40:00 \n",
"\n",
" ADMISSION_TYPE HOSPITAL_EXPIRE_FLAG GENDER DOB DOD \\\n",
"0 EMERGENCY 1 F 2132-02-21 2198-02-18 \n",
"1 EMERGENCY 0 M 2130-09-30 NaT \n",
"2 ELECTIVE 0 M 2048-05-26 NaT \n",
"3 EMERGENCY 0 F 2074-11-30 NaT \n",
"4 EMERGENCY 0 M 2119-11-21 NaT \n",
"... ... ... ... ... ... \n",
"61527 EMERGENCY 0 M 2027-03-02 NaT \n",
"61528 EMERGENCY 0 F 1855-12-07 NaT \n",
"61529 ELECTIVE 0 F 2107-01-29 2162-01-05 \n",
"61530 ELECTIVE 0 F 2115-05-23 NaT \n",
"61531 EMERGENCY 0 M 2123-03-01 NaT \n",
"\n",
" DOD_HOSP EXPIRE_FLAG AGE \n",
"0 2198-02-18 00:00:00 1 66.019178 \n",
"1 NaN 0 40.126027 \n",
"2 NaN 0 80.128767 \n",
"3 NaN 0 45.715068 \n",
"4 NaN 0 67.139726 \n",
"... ... ... ... \n",
"61527 NaN 0 77.161644 \n",
"61528 NaN 0 300.200000 \n",
"61529 NaN 1 53.128767 \n",
"61530 NaN 0 67.884932 \n",
"61531 NaN 0 34.241096 \n",
"\n",
"[61532 rows x 16 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#calculate age\n",
"import datetime as DT\n",
"icu['ADMITTIME'] = pd.to_datetime(icu['ADMITTIME']).dt.date\n",
"icu['DOB'] = pd.to_datetime(icu['DOB']).dt.date\n",
"\n",
"#icu['AGE'] = ((icu['ADMITTIME']-icu['DOB']).dt.days) //365\n",
"icu['AGE'] = icu.apply(lambda e: (e['ADMITTIME'] - e['DOB']).days/365, axis=1)\n",
"icu"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SUBJECT_ID</th>\n",
" <th>HADM_ID</th>\n",
" <th>ICUSTAY_ID</th>\n",
" <th>LOS</th>\n",
" <th>HOSPITAL_EXPIRE_FLAG</th>\n",
" <th>EXPIRE_FLAG</th>\n",
" <th>AGE</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>61532.000000</td>\n",
" <td>61532.000000</td>\n",
" <td>61532.000000</td>\n",
" <td>61522.000000</td>\n",
" <td>61532.000000</td>\n",
" <td>61532.000000</td>\n",
" <td>61532.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>33888.605912</td>\n",
" <td>149954.470649</td>\n",
" <td>249962.710248</td>\n",
" <td>4.917972</td>\n",
" <td>0.107408</td>\n",
" <td>0.393145</td>\n",
" <td>64.925951</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>28127.690913</td>\n",
" <td>28898.895904</td>\n",
" <td>28890.574867</td>\n",
" <td>9.638784</td>\n",
" <td>0.309633</td>\n",
" <td>0.488453</td>\n",
" <td>56.951788</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>2.000000</td>\n",
" <td>100001.000000</td>\n",
" <td>200001.000000</td>\n",
" <td>0.000100</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>12047.500000</td>\n",
" <td>124933.750000</td>\n",
" <td>224935.500000</td>\n",
" <td>1.108025</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>44.374658</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>24280.500000</td>\n",
" <td>149911.500000</td>\n",
" <td>249940.000000</td>\n",
" <td>2.092250</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>62.112329</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>54191.500000</td>\n",
" <td>174997.250000</td>\n",
" <td>274972.500000</td>\n",
" <td>4.483175</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>76.101370</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>99999.000000</td>\n",
" <td>199999.000000</td>\n",
" <td>299999.000000</td>\n",
" <td>173.072500</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>311.767123</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID LOS \\\n",
"count 61532.000000 61532.000000 61532.000000 61522.000000 \n",
"mean 33888.605912 149954.470649 249962.710248 4.917972 \n",
"std 28127.690913 28898.895904 28890.574867 9.638784 \n",
"min 2.000000 100001.000000 200001.000000 0.000100 \n",
"25% 12047.500000 124933.750000 224935.500000 1.108025 \n",
"50% 24280.500000 149911.500000 249940.000000 2.092250 \n",
"75% 54191.500000 174997.250000 274972.500000 4.483175 \n",
"max 99999.000000 199999.000000 299999.000000 173.072500 \n",
"\n",
" HOSPITAL_EXPIRE_FLAG EXPIRE_FLAG AGE \n",
"count 61532.000000 61532.000000 61532.000000 \n",
"mean 0.107408 0.393145 64.925951 \n",
"std 0.309633 0.488453 56.951788 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 44.374658 \n",
"50% 0.000000 0.000000 62.112329 \n",
"75% 0.000000 1.000000 76.101370 \n",
"max 1.000000 1.000000 311.767123 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"icu.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\abebu\\DS\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (6,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n",
"C:\\Users\\abebu\\DS\\lib\\site-packages\\numpy\\lib\\arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"charts=pd.read_csv(\"charts_ioi.csv\", header=0, index_col=0) "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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" <td>58.0</td>\n",
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" <tr>\n",
" <td>16</td>\n",
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" <td>2134-05-12 13:00:00</td>\n",
" <td>137</td>\n",
" <td>137.0</td>\n",
" <td>mmHg</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>36</td>\n",
" <td>165660</td>\n",
" <td>241249.0</td>\n",
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" <td>2134-05-12 14:00:00</td>\n",
" <td>85</td>\n",
" <td>85.0</td>\n",
" <td>bpm</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>2133-07-28 04:37:00</td>\n",
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],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \\\n",
"1 36 165660 241249.0 223835 2134-05-12 12:00:00 \n",
"9 36 165660 241249.0 220224 2134-05-12 12:35:00 \n",
"15 36 165660 241249.0 220045 2134-05-12 13:00:00 \n",
"16 36 165660 241249.0 220179 2134-05-12 13:00:00 \n",
"28 36 165660 241249.0 220045 2134-05-12 14:00:00 \n",
"... ... ... ... ... ... \n",
"330712398 99781 167791 239830.0 223900 2133-07-28 04:37:00 \n",
"330712399 99781 167791 239830.0 223901 2133-07-28 04:37:00 \n",
"330712445 99781 167791 239830.0 220739 2133-07-28 08:00:00 \n",
"330712456 99781 167791 239830.0 223900 2133-07-28 08:00:00 \n",
"330712457 99781 167791 239830.0 223901 2133-07-28 08:00:00 \n",
"\n",
" VALUE VALUENUM VALUEUOM \n",
"1 100 100.0 NaN \n",
"9 58 58.0 mmHg \n",
"15 86 86.0 bpm \n",
"16 137 137.0 mmHg \n",
"28 85 85.0 bpm \n",
"... ... ... ... \n",
"330712398 Oriented 5.0 NaN \n",
"330712399 Obeys Commands 6.0 NaN \n",
"330712445 Spontaneously 4.0 NaN \n",
"330712456 Oriented 5.0 NaN \n",
"330712457 Obeys Commands 6.0 NaN \n",
"\n",
"[25316346 rows x 8 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 25316346 entries, 1 to 330712457\n",
"Data columns (total 8 columns):\n",
"SUBJECT_ID int64\n",
"HADM_ID int64\n",
"ICUSTAY_ID float64\n",
"ITEMID int64\n",
"CHARTTIME object\n",
"VALUE object\n",
"VALUENUM float64\n",
"VALUEUOM object\n",
"dtypes: float64(2), int64(3), object(3)\n",
"memory usage: 1.7+ GB\n"
]
}
],
"source": [
"#charts=charts[charts['SUBJECT_ID'].isin(adm_sample_list)]\n",
"charts.info()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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" <td>85</td>\n",
" <td>85.0</td>\n",
" <td>bpm</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330712398</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223900</td>\n",
" <td>2133-07-28 04:37:00</td>\n",
" <td>Oriented</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330712399</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-28 04:37:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330712445</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>220739</td>\n",
" <td>2133-07-28 08:00:00</td>\n",
" <td>Spontaneously</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330712456</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223900</td>\n",
" <td>2133-07-28 08:00:00</td>\n",
" <td>Oriented</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330712457</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-28 08:00:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>25316346 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \\\n",
"1 36 165660 241249.0 223835 2134-05-12 12:00:00 \n",
"9 36 165660 241249.0 220224 2134-05-12 12:35:00 \n",
"15 36 165660 241249.0 220045 2134-05-12 13:00:00 \n",
"16 36 165660 241249.0 220179 2134-05-12 13:00:00 \n",
"28 36 165660 241249.0 220045 2134-05-12 14:00:00 \n",
"... ... ... ... ... ... \n",
"330712398 99781 167791 239830.0 223900 2133-07-28 04:37:00 \n",
"330712399 99781 167791 239830.0 223901 2133-07-28 04:37:00 \n",
"330712445 99781 167791 239830.0 220739 2133-07-28 08:00:00 \n",
"330712456 99781 167791 239830.0 223900 2133-07-28 08:00:00 \n",
"330712457 99781 167791 239830.0 223901 2133-07-28 08:00:00 \n",
"\n",
" VALUE VALUENUM VALUEUOM \n",
"1 100 100.0 NaN \n",
"9 58 58.0 mmHg \n",
"15 86 86.0 bpm \n",
"16 137 137.0 mmHg \n",
"28 85 85.0 bpm \n",
"... ... ... ... \n",
"330712398 Oriented 5.0 NaN \n",
"330712399 Obeys Commands 6.0 NaN \n",
"330712445 Spontaneously 4.0 NaN \n",
"330712456 Oriented 5.0 NaN \n",
"330712457 Obeys Commands 6.0 NaN \n",
"\n",
"[25316346 rows x 8 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts.sort_values('SUBJECT_ID', ascending=True)\n",
"charts"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"charts=charts.dropna(how='any', subset=['VALUENUM']) # This removes rows with null values for VALUENUM column (a total of 190,141 records removed)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 12487 entries, 457 to 14522\n",
"Data columns (total 3 columns):\n",
"ITEMID 12487 non-null int64\n",
"LABEL 12483 non-null object\n",
"CATEGORY 6049 non-null object\n",
"dtypes: int64(1), object(2)\n",
"memory usage: 390.2+ KB\n"
]
}
],
"source": [
"d_c=pd.read_csv('D_ITEMS.csv', header=0, index_col=0) \n",
"d_c=d_c[['ITEMID', 'LABEL', 'CATEGORY']]\n",
"d_c.info()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def item_merge(table1, table2):\n",
" return table1.merge(table2, how='inner', left_on=['ITEMID'], right_on=['ITEMID'])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"charts=item_merge(charts, d_c)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SUBJECT_ID</th>\n",
" <th>HADM_ID</th>\n",
" <th>ICUSTAY_ID</th>\n",
" <th>ITEMID</th>\n",
" <th>CHARTTIME</th>\n",
" <th>VALUE</th>\n",
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" <td>2134-05-12 12:00:00</td>\n",
" <td>100</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" <td>Inspired O2 Fraction</td>\n",
" <td>Respiratory</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>34</td>\n",
" <td>144319</td>\n",
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" <td>2191-02-23 07:31:00</td>\n",
" <td>60</td>\n",
" <td>60.0</td>\n",
" <td>NaN</td>\n",
" <td>Inspired O2 Fraction</td>\n",
" <td>Respiratory</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
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" <td>2134-05-12 07:09:00</td>\n",
" <td>100</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" <td>Inspired O2 Fraction</td>\n",
" <td>Respiratory</td>\n",
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" <tr>\n",
" <td>3</td>\n",
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" <td>290505.0</td>\n",
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" <td>2191-02-23 11:00:00</td>\n",
" <td>60</td>\n",
" <td>60.0</td>\n",
" <td>NaN</td>\n",
" <td>Inspired O2 Fraction</td>\n",
" <td>Respiratory</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>36</td>\n",
" <td>165660</td>\n",
" <td>241249.0</td>\n",
" <td>223835</td>\n",
" <td>2134-05-13 16:00:00</td>\n",
" <td>50</td>\n",
" <td>50.0</td>\n",
" <td>NaN</td>\n",
" <td>Inspired O2 Fraction</td>\n",
" <td>Respiratory</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-24 19:00:00</td>\n",
" <td>No response</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>GCS - Motor Response</td>\n",
" <td>Neurological</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25126201</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-25 20:00:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>GCS - Motor Response</td>\n",
" <td>Neurological</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25126202</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-27 00:00:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>GCS - Motor Response</td>\n",
" <td>Neurological</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25126203</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-28 04:37:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>GCS - Motor Response</td>\n",
" <td>Neurological</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25126204</td>\n",
" <td>99781</td>\n",
" <td>167791</td>\n",
" <td>239830.0</td>\n",
" <td>223901</td>\n",
" <td>2133-07-28 08:00:00</td>\n",
" <td>Obeys Commands</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>GCS - Motor Response</td>\n",
" <td>Neurological</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>25126205 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \\\n",
"0 36 165660 241249.0 223835 2134-05-12 12:00:00 \n",
"1 34 144319 290505.0 223835 2191-02-23 07:31:00 \n",
"2 36 165660 241249.0 223835 2134-05-12 07:09:00 \n",
"3 34 144319 290505.0 223835 2191-02-23 11:00:00 \n",
"4 36 165660 241249.0 223835 2134-05-13 16:00:00 \n",
"... ... ... ... ... ... \n",
"25126200 99781 167791 239830.0 223901 2133-07-24 19:00:00 \n",
"25126201 99781 167791 239830.0 223901 2133-07-25 20:00:00 \n",
"25126202 99781 167791 239830.0 223901 2133-07-27 00:00:00 \n",
"25126203 99781 167791 239830.0 223901 2133-07-28 04:37:00 \n",
"25126204 99781 167791 239830.0 223901 2133-07-28 08:00:00 \n",
"\n",
" VALUE VALUENUM VALUEUOM LABEL \\\n",
"0 100 100.0 NaN Inspired O2 Fraction \n",
"1 60 60.0 NaN Inspired O2 Fraction \n",
"2 100 100.0 NaN Inspired O2 Fraction \n",
"3 60 60.0 NaN Inspired O2 Fraction \n",
"4 50 50.0 NaN Inspired O2 Fraction \n",
"... ... ... ... ... \n",
"25126200 No response 1.0 NaN GCS - Motor Response \n",
"25126201 Obeys Commands 6.0 NaN GCS - Motor Response \n",
"25126202 Obeys Commands 6.0 NaN GCS - Motor Response \n",
"25126203 Obeys Commands 6.0 NaN GCS - Motor Response \n",
"25126204 Obeys Commands 6.0 NaN GCS - Motor Response \n",
"\n",
" CATEGORY \n",
"0 Respiratory \n",
"1 Respiratory \n",
"2 Respiratory \n",
"3 Respiratory \n",
"4 Respiratory \n",
"... ... \n",
"25126200 Neurological \n",
"25126201 Neurological \n",
"25126202 Neurological \n",
"25126203 Neurological \n",
"25126204 Neurological \n",
"\n",
"[25126205 rows x 10 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"charts['LABEL'] = charts['LABEL'].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"charts['LABEL'].cat.set_categories(['ART BP Systolic', 'NBP [Systolic]', 'Arterial BP [Systolic]', \n",
" 'BP Cuff [Systolic]', 'Manual BP [Systolic]', 'BP UAC [Systolic]', 'BP Right Leg [Systolic]',\n",
" 'Pulmonary Artery Pressure systolic', 'Non Invasive Blood Pressure systolic', 'Arterial Blood Pressure systolic', \n",
" 'Heart Rate', 'Heart Rate Alarm - Low', 'Heart Rhythm', 'Heart rate Alarm - High', \n",
" 'WBC (4-11,000)', 'WBC (4-11,000)', 'WBC 4.0-11.0', 'WBC', \n",
" 'BUN', 'BUN (6-20)', 'BUN (6-20)', \n",
" 'Potassium (whole blood)', 'Potassium (3.5-5.3)', 'Potassium (3.5-5.3)',\n",
" 'Arterial O2 pressure', 'Arterial PaO2', 'Inspired O2 Fraction', 'FIO2', 'FIO2 Alarm [High]', 'FIO2 Alarm [Low]',\n",
" 'FIO2 Alarm-High', 'FIO2 [Meas]', \n",
" 'Blood Temperature CCO (C)',\n",
" 'Temperature Fahrenheit', 'Temperature Celsius', 'Skin [Temperature]', 'Temperature C', 'Temperature C (calc)',\n",
" 'Temperature F', 'Temperature F (calc)',\n",
" 'Sodium (serum)', 'Sodium', 'Sodium (135-148)', 'Sodium (135-148)', 'Sodium (whole blood)', \n",
" 'HCO3 (serum)',\n",
" 'Total Bilirubin', 'Total Bili', 'Direct Bili (0-0.3)',\n",
" 'GCS - Eye Opening', 'GCS - Motor Response', 'GCS - Verbal Response', 'GCS Total', ], inplace=True) "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1761aaf3fc8>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts.groupby(\"LABEL\")['VALUENUM'].count().sort_values().plot(kind='bar', color='green')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# convert from Temperature Celsius Temperature Fahrenheit\n",
"charts[charts.LABEL.isin(['Temp Axillary [F]','Temp Rectal [F]',\n",
" 'Blood Temperature CCO (C)', 'Temperature Fahrenheit', 'Temperature Celsius', 'Skin [Temperature]', \n",
" 'Temperature C', 'Temperature C (calc)', 'Temperature F', 'Temperature F (calc)',])]\n",
"charts.loc[charts.LABEL== 'Temperature Fahrenheit', 'VALUENUM'] = ((charts.VALUENUM -32) * 5./9)\n",
"charts.loc[charts.LABEL== 'Temperature F', 'VALUENUM'] = ((charts.VALUENUM -32) * 5./9)\n",
"charts.loc[charts.LABEL== 'Temperature F (calc)', 'VALUENUM'] = ((charts.VALUENUM -32) * 5./9)\n",
"charts.loc[charts.LABEL== 'Temp Axillary [F]', 'VALUENUM'] = ((charts.VALUENUM -32) * 5./9)\n",
"charts.loc[charts.LABEL== 'Temp Rectal [F]', 'VALUENUM'] = ((charts.VALUENUM -32) * 5./9)\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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" <td>97.3</td>\n",
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" <td>?F</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>4751004</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>4751005</td>\n",
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" <td>2157-10-22 16:00:00</td>\n",
" <td>98.4</td>\n",
" <td>36.888889</td>\n",
" <td>?F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4751006</td>\n",
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" <td>2157-10-21 12:05:00</td>\n",
" <td>95.8</td>\n",
" <td>35.444444</td>\n",
" <td>?F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4751007</td>\n",
" <td>23</td>\n",
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" <td>234044.0</td>\n",
" <td>223761</td>\n",
" <td>2157-10-21 14:00:00</td>\n",
" <td>97.2</td>\n",
" <td>36.222222</td>\n",
" <td>?F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
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" <tr>\n",
" <td>16668142</td>\n",
" <td>32810</td>\n",
" <td>189751</td>\n",
" <td>204923.0</td>\n",
" <td>679</td>\n",
" <td>2100-09-19 07:00:00</td>\n",
" <td>99.860000610351562</td>\n",
" <td>37.700000</td>\n",
" <td>Deg. F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16668143</td>\n",
" <td>32810</td>\n",
" <td>189751</td>\n",
" <td>204923.0</td>\n",
" <td>679</td>\n",
" <td>2100-09-20 03:00:00</td>\n",
" <td>97.519996643066406</td>\n",
" <td>36.399998</td>\n",
" <td>Deg. F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16668144</td>\n",
" <td>32805</td>\n",
" <td>124834</td>\n",
" <td>252568.0</td>\n",
" <td>679</td>\n",
" <td>2182-11-21 11:00:00</td>\n",
" <td>99.860000610351562</td>\n",
" <td>37.700000</td>\n",
" <td>Deg. F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16668145</td>\n",
" <td>32805</td>\n",
" <td>124834</td>\n",
" <td>252568.0</td>\n",
" <td>679</td>\n",
" <td>2182-11-22 08:00:00</td>\n",
" <td>98.599998474121094</td>\n",
" <td>36.999999</td>\n",
" <td>Deg. F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16668146</td>\n",
" <td>32777</td>\n",
" <td>105007</td>\n",
" <td>202322.0</td>\n",
" <td>679</td>\n",
" <td>2113-10-13 04:00:00</td>\n",
" <td>100.22000122070312</td>\n",
" <td>37.900001</td>\n",
" <td>Deg. F</td>\n",
" <td>Temperature Celsius</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2945125 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \\\n",
"4751003 36 165660 241249.0 223761 2134-05-12 16:00:00 \n",
"4751004 34 144319 290505.0 223761 2191-02-23 08:00:00 \n",
"4751005 23 124321 234044.0 223761 2157-10-22 16:00:00 \n",
"4751006 23 124321 234044.0 223761 2157-10-21 12:05:00 \n",
"4751007 23 124321 234044.0 223761 2157-10-21 14:00:00 \n",
"... ... ... ... ... ... \n",
"16668142 32810 189751 204923.0 679 2100-09-19 07:00:00 \n",
"16668143 32810 189751 204923.0 679 2100-09-20 03:00:00 \n",
"16668144 32805 124834 252568.0 679 2182-11-21 11:00:00 \n",
"16668145 32805 124834 252568.0 679 2182-11-22 08:00:00 \n",
"16668146 32777 105007 202322.0 679 2113-10-13 04:00:00 \n",
"\n",
" VALUE VALUENUM VALUEUOM LABEL \\\n",
"4751003 97.3 36.277778 ?F Temperature Celsius \n",
"4751004 97 36.111111 ?F Temperature Celsius \n",
"4751005 98.4 36.888889 ?F Temperature Celsius \n",
"4751006 95.8 35.444444 ?F Temperature Celsius \n",
"4751007 97.2 36.222222 ?F Temperature Celsius \n",
"... ... ... ... ... \n",
"16668142 99.860000610351562 37.700000 Deg. F Temperature Celsius \n",
"16668143 97.519996643066406 36.399998 Deg. F Temperature Celsius \n",
"16668144 99.860000610351562 37.700000 Deg. F Temperature Celsius \n",
"16668145 98.599998474121094 36.999999 Deg. F Temperature Celsius \n",
"16668146 100.22000122070312 37.900001 Deg. F Temperature Celsius \n",
"\n",
" CATEGORY \n",
"4751003 Routine Vital Signs \n",
"4751004 Routine Vital Signs \n",
"4751005 Routine Vital Signs \n",
"4751006 Routine Vital Signs \n",
"4751007 Routine Vital Signs \n",
"... ... \n",
"16668142 NaN \n",
"16668143 NaN \n",
"16668144 NaN \n",
"16668145 NaN \n",
"16668146 NaN \n",
"\n",
"[2945125 rows x 10 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# now that both 'Temperature Fahrenheit', 'Temperature Celsius' have the same unit, lets make sure they have the same label name. \n",
"charts['LABEL'].mask(charts['LABEL']== 'Temperature Fahrenheit', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temperature F', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temperature F (calc)', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Blood Temperature CCO (C)', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Skin [Temperature]', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temperature C', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temperature C (calc)', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temp Axillary [F]', 'Temperature Celsius', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Temp Rectal [F]', 'Temperature Celsius', inplace=True)\n",
"charts[charts.LABEL.isin(['Temperature Celsius'])]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>HADM_ID</th>\n",
" <th>ICUSTAY_ID</th>\n",
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" <td>220179</td>\n",
" <td>2134-05-12 13:00:00</td>\n",
" <td>137</td>\n",
" <td>137.0</td>\n",
" <td>mmHg</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>Routine Vital Signs</td>\n",
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" <tr>\n",
" <td>3460516</td>\n",
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" <td>2134-05-12 15:00:00</td>\n",
" <td>130</td>\n",
" <td>130.0</td>\n",
" <td>mmHg</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>Routine Vital Signs</td>\n",
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" <tr>\n",
" <td>3460518</td>\n",
" <td>36</td>\n",
" <td>165660</td>\n",
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" <td>220179</td>\n",
" <td>2134-05-12 16:00:00</td>\n",
" <td>117</td>\n",
" <td>117.0</td>\n",
" <td>mmHg</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>Routine Vital Signs</td>\n",
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" <tr>\n",
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" <td>144319</td>\n",
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" <td>2191-02-23 07:34:00</td>\n",
" <td>135</td>\n",
" <td>135.0</td>\n",
" <td>mmHg</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
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" <td>171518</td>\n",
" <td>207607.0</td>\n",
" <td>3325</td>\n",
" <td>2164-12-28 02:00:00</td>\n",
" <td>49</td>\n",
" <td>49.0</td>\n",
" <td>Breath</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23248184</td>\n",
" <td>32324</td>\n",
" <td>171518</td>\n",
" <td>207607.0</td>\n",
" <td>3325</td>\n",
" <td>2164-12-28 03:00:00</td>\n",
" <td>43</td>\n",
" <td>43.0</td>\n",
" <td>Breath</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23248185</td>\n",
" <td>32324</td>\n",
" <td>171518</td>\n",
" <td>207607.0</td>\n",
" <td>3325</td>\n",
" <td>2164-12-28 04:00:00</td>\n",
" <td>49</td>\n",
" <td>49.0</td>\n",
" <td>Breath</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23248186</td>\n",
" <td>32324</td>\n",
" <td>171518</td>\n",
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" <td>3325</td>\n",
" <td>2164-12-28 05:00:00</td>\n",
" <td>49</td>\n",
" <td>49.0</td>\n",
" <td>Breath</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23248187</td>\n",
" <td>32324</td>\n",
" <td>171518</td>\n",
" <td>207607.0</td>\n",
" <td>3325</td>\n",
" <td>2164-12-28 06:00:00</td>\n",
" <td>44</td>\n",
" <td>44.0</td>\n",
" <td>Breath</td>\n",
" <td>ART BP Systolic</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6494258 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"3460515 36 165660 241249.0 220179 2134-05-12 13:00:00 137 \n",
"3460516 36 165660 241249.0 220179 2134-05-12 14:00:00 118 \n",
"3460517 36 165660 241249.0 220179 2134-05-12 15:00:00 130 \n",
"3460518 36 165660 241249.0 220179 2134-05-12 16:00:00 117 \n",
"3460519 34 144319 290505.0 220179 2191-02-23 07:34:00 135 \n",
"... ... ... ... ... ... ... \n",
"23248183 32324 171518 207607.0 3325 2164-12-28 02:00:00 49 \n",
"23248184 32324 171518 207607.0 3325 2164-12-28 03:00:00 43 \n",
"23248185 32324 171518 207607.0 3325 2164-12-28 04:00:00 49 \n",
"23248186 32324 171518 207607.0 3325 2164-12-28 05:00:00 49 \n",
"23248187 32324 171518 207607.0 3325 2164-12-28 06:00:00 44 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"3460515 137.0 mmHg ART BP Systolic Routine Vital Signs \n",
"3460516 118.0 mmHg ART BP Systolic Routine Vital Signs \n",
"3460517 130.0 mmHg ART BP Systolic Routine Vital Signs \n",
"3460518 117.0 mmHg ART BP Systolic Routine Vital Signs \n",
"3460519 135.0 mmHg ART BP Systolic Routine Vital Signs \n",
"... ... ... ... ... \n",
"23248183 49.0 Breath ART BP Systolic NaN \n",
"23248184 43.0 Breath ART BP Systolic NaN \n",
"23248185 49.0 Breath ART BP Systolic NaN \n",
"23248186 49.0 Breath ART BP Systolic NaN \n",
"23248187 44.0 Breath ART BP Systolic NaN \n",
"\n",
"[6494258 rows x 10 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['ART BP Systolic', 'NBP [Systolic]', 'Arterial BP [Systolic]', \n",
" 'BP Cuff [Systolic]', 'Manual BP [Systolic]', 'BP UAC [Systolic]', 'BP Right Leg [Systolic]',\n",
" 'Pulmonary Artery Pressure systolic', 'Non Invasive Blood Pressure systolic', 'Arterial Blood Pressure systolic'])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'NBP [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Arterial BP [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'BP Cuff [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Manual BP [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'BP UAC [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'BP Right Leg [Systolic]', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Pulmonary Artery Pressure systolic', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Non Invasive Blood Pressure systolic', 'ART BP Systolic', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Arterial Blood Pressure systolic', 'ART BP Systolic', inplace=True)\n",
"charts[charts.LABEL.isin(['ART BP Systolic'])]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
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" <td>91.0</td>\n",
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" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>34</td>\n",
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" <td>220045</td>\n",
" <td>2191-02-23 07:34:00</td>\n",
" <td>44</td>\n",
" <td>44.0</td>\n",
" <td>bpm</td>\n",
" <td>Heart Rate</td>\n",
" <td>Routine Vital Signs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <td>13435640</td>\n",
" <td>32786</td>\n",
" <td>165351</td>\n",
" <td>275017.0</td>\n",
" <td>211</td>\n",
" <td>2152-11-16 14:45:00</td>\n",
" <td>55</td>\n",
" <td>55.0</td>\n",
" <td>BPM</td>\n",
" <td>Heart Rate</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13435641</td>\n",
" <td>32775</td>\n",
" <td>128184</td>\n",
" <td>292368.0</td>\n",
" <td>211</td>\n",
" <td>2200-09-02 02:00:00</td>\n",
" <td>96</td>\n",
" <td>96.0</td>\n",
" <td>BPM</td>\n",
" <td>Heart Rate</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13435642</td>\n",
" <td>32775</td>\n",
" <td>128184</td>\n",
" <td>292368.0</td>\n",
" <td>211</td>\n",
" <td>2200-09-02 21:00:00</td>\n",
" <td>87</td>\n",
" <td>87.0</td>\n",
" <td>BPM</td>\n",
" <td>Heart Rate</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13435643</td>\n",
" <td>32777</td>\n",
" <td>105007</td>\n",
" <td>202322.0</td>\n",
" <td>211</td>\n",
" <td>2113-10-12 12:15:00</td>\n",
" <td>78</td>\n",
" <td>78.0</td>\n",
" <td>BPM</td>\n",
" <td>Heart Rate</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13435644</td>\n",
" <td>32788</td>\n",
" <td>111585</td>\n",
" <td>273993.0</td>\n",
" <td>211</td>\n",
" <td>2128-03-06 19:00:00</td>\n",
" <td>76</td>\n",
" <td>76.0</td>\n",
" <td>BPM</td>\n",
" <td>Heart Rate</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8373497 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"698290 36 165660 241249.0 220045 2134-05-12 13:00:00 86 \n",
"698291 36 165660 241249.0 220045 2134-05-12 14:00:00 85 \n",
"698292 36 165660 241249.0 220045 2134-05-12 15:00:00 87 \n",
"698293 36 165660 241249.0 220045 2134-05-12 16:00:00 91 \n",
"698294 34 144319 290505.0 220045 2191-02-23 07:34:00 44 \n",
"... ... ... ... ... ... ... \n",
"13435640 32786 165351 275017.0 211 2152-11-16 14:45:00 55 \n",
"13435641 32775 128184 292368.0 211 2200-09-02 02:00:00 96 \n",
"13435642 32775 128184 292368.0 211 2200-09-02 21:00:00 87 \n",
"13435643 32777 105007 202322.0 211 2113-10-12 12:15:00 78 \n",
"13435644 32788 111585 273993.0 211 2128-03-06 19:00:00 76 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"698290 86.0 bpm Heart Rate Routine Vital Signs \n",
"698291 85.0 bpm Heart Rate Routine Vital Signs \n",
"698292 87.0 bpm Heart Rate Routine Vital Signs \n",
"698293 91.0 bpm Heart Rate Routine Vital Signs \n",
"698294 44.0 bpm Heart Rate Routine Vital Signs \n",
"... ... ... ... ... \n",
"13435640 55.0 BPM Heart Rate NaN \n",
"13435641 96.0 BPM Heart Rate NaN \n",
"13435642 87.0 BPM Heart Rate NaN \n",
"13435643 78.0 BPM Heart Rate NaN \n",
"13435644 76.0 BPM Heart Rate NaN \n",
"\n",
"[8373497 rows x 10 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['Heart Rate', 'Heart Rate Alarm - Low','Heart rate Alarm - High'])]\n",
"\n",
"charts['LABEL'].mask(charts['LABEL']== 'Heart Rate Alarm - Low', 'Heart Rate', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Heart rate Alarm - High', 'Heart Rate', inplace=True)\n",
"charts[charts.LABEL.isin(['Heart Rate'])]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>HADM_ID</th>\n",
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" <td>5.4</td>\n",
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" <td>Potassium (whole blood)</td>\n",
" <td>Labs</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>3792</td>\n",
" <td>2176-12-09 18:30:00</td>\n",
" <td>5.6</td>\n",
" <td>5.6</td>\n",
" <td>NaN</td>\n",
" <td>Potassium (whole blood)</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19117630</td>\n",
" <td>32651</td>\n",
" <td>157077</td>\n",
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" <td>2176-11-11 01:00:00</td>\n",
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" <td>2176-11-13 01:15:00</td>\n",
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" <td>Potassium (whole blood)</td>\n",
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" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>Potassium (whole blood)</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19117633</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>3792</td>\n",
" <td>2162-10-24 00:05:00</td>\n",
" <td>4.7</td>\n",
" <td>4.7</td>\n",
" <td>NaN</td>\n",
" <td>Potassium (whole blood)</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>386542 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"7493983 107 182383 252542.0 227464 2121-11-30 19:47:00 6 \n",
"7493984 109 147469 253139.0 227464 2141-06-11 14:19:00 4.3 \n",
"7493985 109 172335 262652.0 227464 2141-09-21 04:40:00 5 \n",
"7493986 109 126055 236124.0 227464 2141-10-15 20:03:00 4 \n",
"7493987 109 158995 241296.0 227464 2142-02-25 17:10:00 5.4 \n",
"... ... ... ... ... ... ... \n",
"19117629 32651 157077 242024.0 3792 2176-12-09 18:30:00 5.6 \n",
"19117630 32651 157077 242024.0 3792 2176-11-11 01:00:00 4.5 \n",
"19117631 32651 157077 242024.0 3792 2176-11-13 01:15:00 5.1 \n",
"19117632 32651 157077 242024.0 3792 2176-11-15 03:55:00 5.1 \n",
"19117633 32806 104049 262007.0 3792 2162-10-24 00:05:00 4.7 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"7493983 6.0 mEq/L Potassium (whole blood) Labs \n",
"7493984 4.3 mEq/L Potassium (whole blood) Labs \n",
"7493985 5.0 mEq/L Potassium (whole blood) Labs \n",
"7493986 4.0 mEq/L Potassium (whole blood) Labs \n",
"7493987 5.4 mEq/L Potassium (whole blood) Labs \n",
"... ... ... ... ... \n",
"19117629 5.6 NaN Potassium (whole blood) Chemistry \n",
"19117630 4.5 NaN Potassium (whole blood) Chemistry \n",
"19117631 5.1 NaN Potassium (whole blood) Chemistry \n",
"19117632 5.1 NaN Potassium (whole blood) Chemistry \n",
"19117633 4.7 NaN Potassium (whole blood) Chemistry \n",
"\n",
"[386542 rows x 10 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['Potassium (whole blood)', 'Potassium (3.5-5.3)', 'Potassium (3.5-5.3)', 'Potassium (serum)'])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'Potassium (3.5-5.3)', 'Potassium (whole blood)', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Potassium (3.5-5.3)', 'Potassium (whole blood)', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Potassium (serum)', 'Potassium (whole blood)', inplace=True)\n",
"#cd.VALUEUOM.fillna('mmHg', inplace=True) # this is not necessary as all have the same unit (mmHg)\n",
"charts[charts.LABEL.isin(['Potassium (whole blood)'])]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <td>19133382</td>\n",
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" <td>3803</td>\n",
" <td>2162-10-24 00:05:00</td>\n",
" <td>139</td>\n",
" <td>139.0</td>\n",
" <td>NaN</td>\n",
" <td>Sodium (serum)</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
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"</table>\n",
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"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"6988199 23 124321 234044.0 220645 2157-10-21 14:40:00 144 \n",
"6988200 23 124321 234044.0 220645 2157-10-22 03:21:00 144 \n",
"6988201 36 165660 241249.0 220645 2134-05-12 08:33:00 139 \n",
"6988202 34 144319 290505.0 220645 2191-02-23 10:48:00 139 \n",
"6988203 34 144319 290505.0 220645 2191-02-24 04:32:00 142 \n",
"... ... ... ... ... ... ... \n",
"19133381 32651 157077 242024.0 3803 2176-12-09 18:30:00 141 \n",
"19133382 32651 157077 242024.0 3803 2176-11-11 01:00:00 141 \n",
"19133383 32651 157077 242024.0 3803 2176-11-13 01:15:00 137 \n",
"19133384 32651 157077 242024.0 3803 2176-11-15 03:55:00 135 \n",
"19133385 32806 104049 262007.0 3803 2162-10-24 00:05:00 139 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"6988199 144.0 mEq/L Sodium (serum) Labs \n",
"6988200 144.0 mEq/L Sodium (serum) Labs \n",
"6988201 139.0 mEq/L Sodium (serum) Labs \n",
"6988202 139.0 mEq/L Sodium (serum) Labs \n",
"6988203 142.0 mEq/L Sodium (serum) Labs \n",
"... ... ... ... ... \n",
"19133381 141.0 NaN Sodium (serum) Chemistry \n",
"19133382 141.0 NaN Sodium (serum) Chemistry \n",
"19133383 137.0 NaN Sodium (serum) Chemistry \n",
"19133384 135.0 NaN Sodium (serum) Chemistry \n",
"19133385 139.0 NaN Sodium (serum) Chemistry \n",
"\n",
"[588665 rows x 10 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['Sodium (serum)', 'Sodium', 'Sodium (135-148)', 'Sodium (135-148)', 'Sodium (whole blood)'])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'Sodium', 'Sodium (serum)', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Sodium (135-148)', 'Sodium (serum)', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Sodium (135-148)', 'Sodium (serum)', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Sodium (whole blood)', 'Sodium (serum)', inplace=True)\n",
"charts[charts.LABEL.isin(['Sodium (serum)'])]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>WBC</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>6854847</td>\n",
" <td>34</td>\n",
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" <td>290505.0</td>\n",
" <td>220546</td>\n",
" <td>2191-02-24 04:32:00</td>\n",
" <td>7.9</td>\n",
" <td>7.90</td>\n",
" <td>K/uL</td>\n",
" <td>WBC</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19167008</td>\n",
" <td>32782</td>\n",
" <td>192581</td>\n",
" <td>201335.0</td>\n",
" <td>4200</td>\n",
" <td>2179-10-03 13:00:00</td>\n",
" <td>11.8</td>\n",
" <td>11.80</td>\n",
" <td>NaN</td>\n",
" <td>WBC</td>\n",
" <td>Heme/Coag</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19167009</td>\n",
" <td>32776</td>\n",
" <td>133625</td>\n",
" <td>222522.0</td>\n",
" <td>4200</td>\n",
" <td>2132-09-14 07:35:00</td>\n",
" <td>20.2</td>\n",
" <td>20.20</td>\n",
" <td>NaN</td>\n",
" <td>WBC</td>\n",
" <td>Heme/Coag</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19167010</td>\n",
" <td>32780</td>\n",
" <td>100455</td>\n",
" <td>287347.0</td>\n",
" <td>4200</td>\n",
" <td>2195-10-02 16:10:00</td>\n",
" <td>15.81</td>\n",
" <td>15.81</td>\n",
" <td>NaN</td>\n",
" <td>WBC</td>\n",
" <td>Heme/Coag</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19167011</td>\n",
" <td>32784</td>\n",
" <td>131855</td>\n",
" <td>243928.0</td>\n",
" <td>4200</td>\n",
" <td>2196-11-08 22:00:00</td>\n",
" <td>6.3</td>\n",
" <td>6.30</td>\n",
" <td>NaN</td>\n",
" <td>WBC</td>\n",
" <td>Heme/Coag</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19167012</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>4200</td>\n",
" <td>2162-10-22 02:25:00</td>\n",
" <td>7.7</td>\n",
" <td>7.70</td>\n",
" <td>NaN</td>\n",
" <td>WBC</td>\n",
" <td>Heme/Coag</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>627368 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"6854843 23 124321 234044.0 220546 2157-10-21 14:40:00 14.8 \n",
"6854844 23 124321 234044.0 220546 2157-10-22 03:21:00 16.4 \n",
"6854845 36 165660 241249.0 220546 2134-05-12 08:33:00 15.3 \n",
"6854846 34 144319 290505.0 220546 2191-02-23 10:48:00 7.1 \n",
"6854847 34 144319 290505.0 220546 2191-02-24 04:32:00 7.9 \n",
"... ... ... ... ... ... ... \n",
"19167008 32782 192581 201335.0 4200 2179-10-03 13:00:00 11.8 \n",
"19167009 32776 133625 222522.0 4200 2132-09-14 07:35:00 20.2 \n",
"19167010 32780 100455 287347.0 4200 2195-10-02 16:10:00 15.81 \n",
"19167011 32784 131855 243928.0 4200 2196-11-08 22:00:00 6.3 \n",
"19167012 32806 104049 262007.0 4200 2162-10-22 02:25:00 7.7 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"6854843 14.80 K/uL WBC Labs \n",
"6854844 16.40 K/uL WBC Labs \n",
"6854845 15.30 K/uL WBC Labs \n",
"6854846 7.10 K/uL WBC Labs \n",
"6854847 7.90 K/uL WBC Labs \n",
"... ... ... ... ... \n",
"19167008 11.80 NaN WBC Heme/Coag \n",
"19167009 20.20 NaN WBC Heme/Coag \n",
"19167010 15.81 NaN WBC Heme/Coag \n",
"19167011 6.30 NaN WBC Heme/Coag \n",
"19167012 7.70 NaN WBC Heme/Coag \n",
"\n",
"[627368 rows x 10 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin([ 'WBC (4-11,000)', 'WBC (4-11,000)', 'WBC 4.0-11.0', 'WBC'])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'WBC (4-11,000)', 'WBC', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'WBC (4-11,000)', 'WBC', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'WBC 4.0-11.0', 'WBC', inplace=True)\n",
"charts[charts.LABEL.isin(['WBC'])]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
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" <td>3737</td>\n",
" <td>2118-02-12 21:00:00</td>\n",
" <td>40</td>\n",
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" <td>32803</td>\n",
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" <td>3737</td>\n",
" <td>2118-02-10 21:00:00</td>\n",
" <td>41</td>\n",
" <td>41.0</td>\n",
" <td>NaN</td>\n",
" <td>BUN</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>501601 rows × 10 columns</p>\n",
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],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"7150876 23 124321 234044.0 225624 2157-10-21 14:40:00 14 \n",
"7150877 23 124321 234044.0 225624 2157-10-22 03:21:00 14 \n",
"7150878 36 165660 241249.0 225624 2134-05-12 08:33:00 19 \n",
"7150879 34 144319 290505.0 225624 2191-02-23 10:48:00 36 \n",
"7150880 34 144319 290505.0 225624 2191-02-24 04:32:00 30 \n",
"... ... ... ... ... ... ... \n",
"19158102 32803 105824 229957.0 3737 2118-02-27 02:15:00 22 \n",
"19158103 32803 105824 229957.0 3737 2118-02-12 21:00:00 40 \n",
"19158104 32803 105824 229957.0 3737 2118-02-13 06:00:00 38 \n",
"19158105 32803 105824 229957.0 3737 2118-02-20 02:15:00 16 \n",
"19158106 32803 105824 229957.0 3737 2118-02-10 21:00:00 41 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"7150876 14.0 mg/dL BUN Labs \n",
"7150877 14.0 mg/dL BUN Labs \n",
"7150878 19.0 mg/dL BUN Labs \n",
"7150879 36.0 mg/dL BUN Labs \n",
"7150880 30.0 mg/dL BUN Labs \n",
"... ... ... ... ... \n",
"19158102 22.0 NaN BUN Chemistry \n",
"19158103 40.0 NaN BUN Chemistry \n",
"19158104 38.0 NaN BUN Chemistry \n",
"19158105 16.0 NaN BUN Chemistry \n",
"19158106 41.0 NaN BUN Chemistry \n",
"\n",
"[501601 rows x 10 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['BUN', 'BUN (6-20)', 'BUN (6-20)'])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'BUN (6-20)', 'BUN', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'BUN (6-20)', 'BUN', inplace=True)\n",
"charts[charts.LABEL.isin(['BUN'])]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
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" <td>80.0</td>\n",
" <td>cmH20</td>\n",
" <td>Inspired O2 Fraction</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"0 36 165660 241249.0 223835 2134-05-12 12:00:00 100 \n",
"1 34 144319 290505.0 223835 2191-02-23 07:31:00 60 \n",
"2 36 165660 241249.0 223835 2134-05-12 07:09:00 100 \n",
"3 34 144319 290505.0 223835 2191-02-23 11:00:00 60 \n",
"4 36 165660 241249.0 223835 2134-05-13 16:00:00 50 \n",
"... ... ... ... ... ... ... \n",
"23427375 32242 163974 203106.0 8517 2121-12-05 17:00:00 60 \n",
"23427376 32242 163974 203106.0 8517 2121-12-05 18:00:00 60 \n",
"23427377 32242 163974 203106.0 8517 2121-12-05 19:00:00 60 \n",
"23427378 32242 163974 203106.0 8517 2121-12-05 20:00:00 60 \n",
"23427379 32242 163974 203106.0 8517 2121-12-14 08:00:00 80 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"0 100.0 NaN Inspired O2 Fraction Respiratory \n",
"1 60.0 NaN Inspired O2 Fraction Respiratory \n",
"2 100.0 NaN Inspired O2 Fraction Respiratory \n",
"3 60.0 NaN Inspired O2 Fraction Respiratory \n",
"4 50.0 NaN Inspired O2 Fraction Respiratory \n",
"... ... ... ... ... \n",
"23427375 60.0 cmH20 Inspired O2 Fraction NaN \n",
"23427376 60.0 cmH20 Inspired O2 Fraction NaN \n",
"23427377 60.0 cmH20 Inspired O2 Fraction NaN \n",
"23427378 60.0 cmH20 Inspired O2 Fraction NaN \n",
"23427379 80.0 cmH20 Inspired O2 Fraction NaN \n",
"\n",
"[2300574 rows x 10 columns]"
]
},
"execution_count": 52,
"metadata": {},
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}
],
"source": [
"charts[charts.LABEL.isin(['Arterial O2 pressure', 'Arterial PaO2', 'Inspired O2 Fraction', 'FIO2', 'FIO2 Alarm [High]',\n",
" 'FIO2 Alarm [Low]', 'FIO2 Alarm-High', 'FIO2 [Meas]'])]\n",
"\n",
"charts['LABEL'].mask(charts['LABEL']== 'Arterial O2 pressure', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Arterial PaO2', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'FIO2', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'FIO2 Alarm [High]', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'FIO2 Alarm [Low]', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'FIO2 Alarm-High', 'Inspired O2 Fraction', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'FIO2 [Meas]', 'Inspired O2 Fraction', inplace=True)\n",
"charts[charts.LABEL.isin(['Inspired O2 Fraction'])]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SUBJECT_ID</th>\n",
" <th>HADM_ID</th>\n",
" <th>ICUSTAY_ID</th>\n",
" <th>ITEMID</th>\n",
" <th>CHARTTIME</th>\n",
" <th>VALUE</th>\n",
" <th>VALUENUM</th>\n",
" <th>VALUEUOM</th>\n",
" <th>LABEL</th>\n",
" <th>CATEGORY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>7456022</td>\n",
" <td>36</td>\n",
" <td>165660</td>\n",
" <td>241249.0</td>\n",
" <td>225690</td>\n",
" <td>2134-05-14 03:20:00</td>\n",
" <td>0.6</td>\n",
" <td>0.6</td>\n",
" <td>mg/dL</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7456023</td>\n",
" <td>107</td>\n",
" <td>174162</td>\n",
" <td>264253.0</td>\n",
" <td>225690</td>\n",
" <td>2122-05-14 21:48:00</td>\n",
" <td>0.1</td>\n",
" <td>0.1</td>\n",
" <td>mg/dL</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7456024</td>\n",
" <td>109</td>\n",
" <td>170149</td>\n",
" <td>266497.0</td>\n",
" <td>225690</td>\n",
" <td>2141-05-24 20:01:00</td>\n",
" <td>0.3</td>\n",
" <td>0.3</td>\n",
" <td>mg/dL</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7456025</td>\n",
" <td>109</td>\n",
" <td>147469</td>\n",
" <td>253139.0</td>\n",
" <td>225690</td>\n",
" <td>2141-06-11 13:07:00</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>mg/dL</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7456026</td>\n",
" <td>109</td>\n",
" <td>147469</td>\n",
" <td>253139.0</td>\n",
" <td>225690</td>\n",
" <td>2141-06-12 14:50:00</td>\n",
" <td>0.4</td>\n",
" <td>0.4</td>\n",
" <td>mg/dL</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Labs</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19155964</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>803</td>\n",
" <td>2162-10-30 00:25:00</td>\n",
" <td>.2</td>\n",
" <td>0.2</td>\n",
" <td>NaN</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19155965</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>803</td>\n",
" <td>2162-10-25 23:40:00</td>\n",
" <td>.3</td>\n",
" <td>0.3</td>\n",
" <td>NaN</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19155966</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>803</td>\n",
" <td>2162-10-31 00:27:00</td>\n",
" <td>.2</td>\n",
" <td>0.2</td>\n",
" <td>NaN</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19155967</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>803</td>\n",
" <td>2162-10-22 13:25:00</td>\n",
" <td>.3</td>\n",
" <td>0.3</td>\n",
" <td>NaN</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19155968</td>\n",
" <td>32806</td>\n",
" <td>104049</td>\n",
" <td>262007.0</td>\n",
" <td>803</td>\n",
" <td>2162-10-24 00:05:00</td>\n",
" <td>.3</td>\n",
" <td>0.3</td>\n",
" <td>NaN</td>\n",
" <td>Total Bilirubin</td>\n",
" <td>Chemistry</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>111652 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME VALUE \\\n",
"7456022 36 165660 241249.0 225690 2134-05-14 03:20:00 0.6 \n",
"7456023 107 174162 264253.0 225690 2122-05-14 21:48:00 0.1 \n",
"7456024 109 170149 266497.0 225690 2141-05-24 20:01:00 0.3 \n",
"7456025 109 147469 253139.0 225690 2141-06-11 13:07:00 0.5 \n",
"7456026 109 147469 253139.0 225690 2141-06-12 14:50:00 0.4 \n",
"... ... ... ... ... ... ... \n",
"19155964 32806 104049 262007.0 803 2162-10-30 00:25:00 .2 \n",
"19155965 32806 104049 262007.0 803 2162-10-25 23:40:00 .3 \n",
"19155966 32806 104049 262007.0 803 2162-10-31 00:27:00 .2 \n",
"19155967 32806 104049 262007.0 803 2162-10-22 13:25:00 .3 \n",
"19155968 32806 104049 262007.0 803 2162-10-24 00:05:00 .3 \n",
"\n",
" VALUENUM VALUEUOM LABEL CATEGORY \n",
"7456022 0.6 mg/dL Total Bilirubin Labs \n",
"7456023 0.1 mg/dL Total Bilirubin Labs \n",
"7456024 0.3 mg/dL Total Bilirubin Labs \n",
"7456025 0.5 mg/dL Total Bilirubin Labs \n",
"7456026 0.4 mg/dL Total Bilirubin Labs \n",
"... ... ... ... ... \n",
"19155964 0.2 NaN Total Bilirubin Chemistry \n",
"19155965 0.3 NaN Total Bilirubin Chemistry \n",
"19155966 0.2 NaN Total Bilirubin Chemistry \n",
"19155967 0.3 NaN Total Bilirubin Chemistry \n",
"19155968 0.3 NaN Total Bilirubin Chemistry \n",
"\n",
"[111652 rows x 10 columns]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts[charts.LABEL.isin(['Total Bilirubin', 'Total Bili', 'Direct Bili (0-0.3)',])]\n",
"charts['LABEL'].mask(charts['LABEL']== 'Total Bili', 'Total Bilirubin', inplace=True)\n",
"charts['LABEL'].mask(charts['LABEL']== 'Direct Bili (0-0.3)', 'Total Bilirubin', inplace=True)\n",
"charts[charts.LABEL.isin(['Total Bilirubin'])]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"charts['LABEL'] = charts['LABEL'].map({'Temperature Fahrenheit': 'Temperature Celsius',\n",
"'Temperature F': 'Temperature Celsius', \n",
"'Temperature F (calc)': 'Temperature Celsius', \n",
"'Blood Temperature CCO (C)': 'Temperature Celsius', \n",
"'Skin [Temperature]': 'Temperature Celsius', \n",
"'Temperature C': 'Temperature Celsius', \n",
"'Temperature C (calc)': 'Temperature Celsius', \n",
"'Temp Axillary [F]': 'Temperature Celsius', \n",
"'Temp Rectal [F]': 'Temperature Celsius',\n",
"'Temperature Celsius': 'Temperature Celsius',\n",
"'NBP [Systolic]': 'ART BP Systolic', \n",
"'Arterial BP [Systolic]' :'ART BP Systolic', \n",
"'BP Cuff [Systolic]': 'ART BP Systolic', \n",
"'Manual BP [Systolic]': 'ART BP Systolic', \n",
"'BP UAC [Systolic]': 'ART BP Systolic',\n",
"'BP Right Leg [Systolic]': 'ART BP Systolic', \n",
"'Pulmonary Artery Pressure systolic': 'ART BP Systolic', \n",
"'Non Invasive Blood Pressure systolic': 'ART BP Systolic',\n",
"'Arterial Blood Pressure systolic': 'ART BP Systolic', \n",
"'ART BP Systolic': 'ART BP Systolic',\n",
"'Heart Rate Alarm - Low': 'Heart Rate',\n",
"'Heart rate Alarm - High': 'Heart Rate',\n",
"'Heart Rate': 'Heart Rate',\n",
"'Potassium (3.5-5.3)': 'Potassium (whole blood)', \n",
"'Potassium (3.5-5.3)': 'Potassium (whole blood)', \n",
"'Potassium (serum)': 'Potassium (whole blood)',\n",
"'Potassium (whole blood)': 'Potassium (whole blood)', \n",
"'Sodium': 'Sodium (serum)', \n",
"'Sodium (135-148)': 'Sodium (serum)', \n",
"'Sodium (135-148)': 'Sodium (serum)', \n",
"'Sodium (whole blood)': 'Sodium (serum)',\n",
"'Sodium (serum)': 'Sodium (serum)',\n",
"'WBC (4-11,000)': 'WBC',\n",
"'WBC (4-11,000)': 'WBC', \n",
"'WBC 4.0-11.0': 'WBC', \n",
"'WBC': 'WBC', \n",
"'BUN (6-20)': 'BUN',\n",
"'BUN (6-20)': 'BUN',\n",
"'BUN': 'BUN',\n",
"'Arterial O2 pressure': 'Inspired O2 Fraction', \n",
"'Arterial PaO2': 'Inspired O2 Fraction', \n",
"'FIO2': 'Inspired O2 Fraction',\n",
"'FIO2 Alarm [High]': 'Inspired O2 Fraction', \n",
"'FIO2 Alarm [Low]': 'Inspired O2 Fraction', \n",
"'FIO2 Alarm-High': 'Inspired O2 Fraction', \n",
"'FIO2 [Meas]': 'Inspired O2 Fraction', \n",
"'Inspired O2 Fraction': 'Inspired O2 Fraction', \n",
"'Total Bili': 'Total Bilirubin', \n",
"'Direct Bili (0-0.3)': 'Total Bilirubin',\n",
"'Total Bilirubin': 'Total Bilirubin', \n",
"'GCS - Eye Opening':'GCS - Eye Opening', \n",
"'GCS - Motor Response':'GCS - Motor Response', \n",
"'GCS - Verbal Response': 'GCS - Verbal Response', \n",
"'GCS Total':'GCS Total', \n",
"'HCO3 (serum)': 'HCO3 (serum)'})"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0, 0, 'Inspired O2 Fraction'),\n",
" Text(0, 0, 'Heart Rate'),\n",
" Text(0, 0, 'ART BP Systolic'),\n",
" Text(0, 0, 'Temperature Celsius'),\n",
" Text(0, 0, 'WBC'),\n",
" Text(0, 0, 'Sodium (serum)'),\n",
" Text(0, 0, 'BUN'),\n",
" Text(0, 0, 'HCO3 (serum)'),\n",
" Text(0, 0, 'Total Bilirubin'),\n",
" Text(0, 0, 'Potassium (whole blood)'),\n",
" Text(0, 0, 'GCS Total'),\n",
" Text(0, 0, 'GCS - Eye Opening'),\n",
" Text(0, 0, 'GCS - Verbal Response'),\n",
" Text(0, 0, 'GCS - Motor Response')]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"f, ax = plt.subplots(figsize=(12, 8))\n",
"sns.countplot(data=charts, x='LABEL', palette='BuGn', ax=ax)\n",
"ax.set_xticklabels(ax.get_xticklabels(),rotation=90)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"charts=charts.pivot_table(values='VALUENUM', index=['SUBJECT_ID', 'HADM_ID', 'ICUSTAY_ID', 'ITEMID', 'CHARTTIME'], columns='LABEL')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
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"text/plain": [
"LABEL ART BP Systolic \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 72.0 \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL BUN \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 15.0 \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL GCS - Eye Opening \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL GCS - Motor Response \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 6.0 \n",
" 2118-01-01 08:16:00 6.0 \n",
" 2118-01-01 12:31:00 6.0 \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL GCS - Verbal Response \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL GCS Total \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL HCO3 (serum) \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 26.0 \n",
"\n",
"LABEL Heart Rate \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 148.0 \n",
" 2138-07-17 20:30:00 131.0 \n",
" 2138-07-17 21:00:00 144.0 \n",
" 2138-07-17 22:00:00 140.0 \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL Inspired O2 Fraction \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL Potassium (whole blood) \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL Sodium (serum) \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL Temperature Celsius \\\n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN \n",
" 2138-07-17 20:30:00 NaN \n",
" 2138-07-17 21:00:00 NaN \n",
" 2138-07-17 22:00:00 NaN \n",
" 3313 2138-07-17 20:30:00 NaN \n",
"... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN \n",
" 2118-01-01 08:16:00 NaN \n",
" 2118-01-01 12:31:00 NaN \n",
" 225624 2118-01-01 05:35:00 NaN \n",
" 227443 2118-01-01 05:35:00 NaN \n",
"\n",
"LABEL Total Bilirubin WBC \n",
"SUBJECT_ID HADM_ID ICUSTAY_ID ITEMID CHARTTIME \n",
"2 163353 243653.0 211 2138-07-17 20:20:00 NaN NaN \n",
" 2138-07-17 20:30:00 NaN NaN \n",
" 2138-07-17 21:00:00 NaN NaN \n",
" 2138-07-17 22:00:00 NaN NaN \n",
" 3313 2138-07-17 20:30:00 NaN NaN \n",
"... ... ... \n",
"99999 113369 246512.0 223901 2117-12-31 16:36:00 NaN NaN \n",
" 2118-01-01 08:16:00 NaN NaN \n",
" 2118-01-01 12:31:00 NaN NaN \n",
" 225624 2118-01-01 05:35:00 NaN NaN \n",
" 227443 2118-01-01 05:35:00 NaN NaN \n",
"\n",
"[25100123 rows x 14 columns]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charts"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def icu_merge(table1, table2):\n",
" return table1.merge(table2, how='left', left_on=['SUBJECT_ID','HADM_ID', 'ICUSTAY_ID'], right_on=['SUBJECT_ID','HADM_ID', 'ICUSTAY_ID'])"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"charts=icu_merge(charts, icu)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"charts.to_csv('charts_f.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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