EDA collaborated with Sook Yee.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1cfe791c",
   "metadata": {},
   "source": [
    "## Datathon "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2afae9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "import functools as ft\n",
    "\n",
    "from scipy import stats\n",
    "# from pandas_profiling import ProfileReport\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc8b8d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 200)\n",
    "pd.set_option('display.max_columns', 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "262ae8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('.\\data\\data-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3181a869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>hospital_expire_flag</th>\n",
       "      <th>deathtime</th>\n",
       "      <th>gender</th>\n",
       "      <th>anchor_age</th>\n",
       "      <th>dod</th>\n",
       "      <th>valuenum</th>\n",
       "      <th>valueuom</th>\n",
       "      <th>itemid</th>\n",
       "      <th>charttime</th>\n",
       "      <th>storetime</th>\n",
       "      <th>icu_intime</th>\n",
       "      <th>icu_outtime</th>\n",
       "      <th>aspirin</th>\n",
       "      <th>p2y12</th>\n",
       "      <th>other_antiplatelet</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10881690</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.9</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>51222</td>\n",
       "      <td>25/4/2163 14:26</td>\n",
       "      <td>25/4/2163 15:20</td>\n",
       "      <td>25/4/2163 10:42</td>\n",
       "      <td>30/4/2163 22:43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10881690</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.8</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>51222</td>\n",
       "      <td>25/4/2163 16:15</td>\n",
       "      <td>25/4/2163 16:48</td>\n",
       "      <td>25/4/2163 10:42</td>\n",
       "      <td>30/4/2163 22:43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10881690</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>51222</td>\n",
       "      <td>25/4/2163 19:36</td>\n",
       "      <td>25/4/2163 20:16</td>\n",
       "      <td>25/4/2163 10:42</td>\n",
       "      <td>30/4/2163 22:43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10881690</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>51222</td>\n",
       "      <td>25/4/2163 23:29</td>\n",
       "      <td>25/4/2163 23:58</td>\n",
       "      <td>25/4/2163 10:42</td>\n",
       "      <td>30/4/2163 22:43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10881690</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>g/dL</td>\n",
       "      <td>51222</td>\n",
       "      <td>26/4/2163 3:36</td>\n",
       "      <td>26/4/2163 4:18</td>\n",
       "      <td>25/4/2163 10:42</td>\n",
       "      <td>30/4/2163 22:43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "      <td>25/4/2163 13:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id  hospital_expire_flag deathtime gender  anchor_age  dod  \\\n",
       "0    10881690                     0       NaN      F          74  NaN   \n",
       "1    10881690                     0       NaN      F          74  NaN   \n",
       "2    10881690                     0       NaN      F          74  NaN   \n",
       "3    10881690                     0       NaN      F          74  NaN   \n",
       "4    10881690                     0       NaN      F          74  NaN   \n",
       "\n",
       "   valuenum valueuom  itemid        charttime        storetime  \\\n",
       "0       4.9     g/dL   51222  25/4/2163 14:26  25/4/2163 15:20   \n",
       "1       4.8     g/dL   51222  25/4/2163 16:15  25/4/2163 16:48   \n",
       "2       8.6     g/dL   51222  25/4/2163 19:36  25/4/2163 20:16   \n",
       "3       9.0     g/dL   51222  25/4/2163 23:29  25/4/2163 23:58   \n",
       "4       8.2     g/dL   51222   26/4/2163 3:36   26/4/2163 4:18   \n",
       "\n",
       "        icu_intime      icu_outtime  aspirin  p2y12  other_antiplatelet  \\\n",
       "0  25/4/2163 10:42  30/4/2163 22:43        0      0                   1   \n",
       "1  25/4/2163 10:42  30/4/2163 22:43        0      0                   1   \n",
       "2  25/4/2163 10:42  30/4/2163 22:43        0      0                   1   \n",
       "3  25/4/2163 10:42  30/4/2163 22:43        0      0                   1   \n",
       "4  25/4/2163 10:42  30/4/2163 22:43        0      0                   1   \n",
       "\n",
       "         starttime         stoptime  \n",
       "0  25/4/2163 13:00  25/4/2163 13:00  \n",
       "1  25/4/2163 13:00  25/4/2163 13:00  \n",
       "2  25/4/2163 13:00  25/4/2163 13:00  \n",
       "3  25/4/2163 13:00  25/4/2163 13:00  \n",
       "4  25/4/2163 13:00  25/4/2163 13:00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1da6c0af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 245361 entries, 0 to 245360\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   subject_id            245361 non-null  int64  \n",
      " 1   hospital_expire_flag  245361 non-null  int64  \n",
      " 2   deathtime             35338 non-null   object \n",
      " 3   gender                245361 non-null  object \n",
      " 4   anchor_age            245361 non-null  int64  \n",
      " 5   dod                   91201 non-null   object \n",
      " 6   valuenum              245361 non-null  float64\n",
      " 7   valueuom              245361 non-null  object \n",
      " 8   itemid                245361 non-null  int64  \n",
      " 9   charttime             245361 non-null  object \n",
      " 10  storetime             245361 non-null  object \n",
      " 11  icu_intime            245361 non-null  object \n",
      " 12  icu_outtime           245361 non-null  object \n",
      " 13  aspirin               245361 non-null  int64  \n",
      " 14  p2y12                 245361 non-null  int64  \n",
      " 15  other_antiplatelet    245361 non-null  int64  \n",
      " 16  starttime             245361 non-null  object \n",
      " 17  stoptime              245319 non-null  object \n",
      "dtypes: float64(1), int64(7), object(10)\n",
      "memory usage: 33.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4c18a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_cohort_df = pd.read_csv('.\\data\\\\final_main_cohort.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae211628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3526 entries, 0 to 3525\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   subject_id            3523 non-null   float64\n",
      " 1   hospital_expire_flag  3523 non-null   float64\n",
      " 2   deathtime             744 non-null    object \n",
      " 3   gender                3523 non-null   object \n",
      " 4   age                   3523 non-null   float64\n",
      " 5   dod                   1729 non-null   object \n",
      " 6   hosp_admittime        3523 non-null   object \n",
      " 7   icu_intime            3523 non-null   object \n",
      " 8   icu_outtime           3523 non-null   object \n",
      " 9   T0                    3526 non-null   object \n",
      " 10  delta_T               3523 non-null   float64\n",
      "dtypes: float64(4), object(7)\n",
      "memory usage: 303.1+ KB\n"
     ]
    }
   ],
   "source": [
    "main_cohort_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aed5c622",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_tmp_df = pd.read_csv('.\\data\\data-tmp_main_cohort.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00977ab4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50934 entries, 0 to 50933\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   subject_id            50934 non-null  int64 \n",
      " 1   hospital_expire_flag  50934 non-null  int64 \n",
      " 2   deathtime             5200 non-null   object\n",
      " 3   gender                50934 non-null  object\n",
      " 4   age                   50934 non-null  int64 \n",
      " 5   dod                   16510 non-null  object\n",
      " 6   hosp_admittime        50934 non-null  object\n",
      " 7   icu_intime            50934 non-null  object\n",
      " 8   icu_outtime           50934 non-null  object\n",
      "dtypes: int64(3), object(6)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "main_tmp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9cbd82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trop_df = pd.read_csv('.\\data\\data-troponin_cohort.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d228d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 286019 entries, 0 to 286018\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   subject_id            286019 non-null  int64  \n",
      " 1   hospital_expire_flag  286019 non-null  int64  \n",
      " 2   deathtime             46448 non-null   object \n",
      " 3   gender                286019 non-null  object \n",
      " 4   age                   286019 non-null  int64  \n",
      " 5   dod                   114290 non-null  object \n",
      " 6   hosp_admittime        286019 non-null  object \n",
      " 7   icu_intime            286019 non-null  object \n",
      " 8   icu_outtime           286019 non-null  object \n",
      " 9   valuenum              286019 non-null  float64\n",
      " 10  valueuom              286019 non-null  object \n",
      " 11  itemid                286019 non-null  int64  \n",
      " 12  lab_time              286019 non-null  object \n",
      "dtypes: float64(1), int64(4), object(8)\n",
      "memory usage: 28.4+ MB\n"
     ]
    }
   ],
   "source": [
    "trop_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0bb263f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trop_df['addmittime_dt'] = pd.to_datetime(trop_df['hosp_admittime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0820bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>addmittime_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>2189-06-27 07:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001217</td>\n",
       "      <td>2157-11-18 22:56:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001725</td>\n",
       "      <td>2110-04-11 15:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001884</td>\n",
       "      <td>2131-01-07 20:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002013</td>\n",
       "      <td>2160-05-18 07:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id       addmittime_dt\n",
       "0    10000980 2189-06-27 07:38:00\n",
       "1    10001217 2157-11-18 22:56:00\n",
       "2    10001725 2110-04-11 15:08:00\n",
       "3    10001884 2131-01-07 20:39:00\n",
       "4    10002013 2160-05-18 07:45:00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addmittime_df = trop_df.groupby('subject_id').first().reset_index()[['subject_id','addmittime_dt']]\n",
    "addmittime_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5d7704e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(addmittime_df,on='subject_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc989908",
   "metadata": {},
   "source": [
    "<span style='color:red'>earliest starttime/stoptime where medication of interest (aspirin/blah blah) is '1' \n",
    "<br> itemid identifying for lab measurement for hemoglo... storetime</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1d6287",
   "metadata": {},
   "source": [
    "(1) earliest time when itemid (aspirin/p2y12/other_antiplatelet)==1\n",
    "<br> (2) earliest time when itemid (hemo)==1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8ba4158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id                   0\n",
       "hospital_expire_flag         0\n",
       "deathtime               210000\n",
       "gender                       0\n",
       "anchor_age                   0\n",
       "dod                     154143\n",
       "valuenum                     0\n",
       "valueuom                     0\n",
       "itemid                       0\n",
       "charttime                    0\n",
       "storetime                    0\n",
       "icu_intime                   0\n",
       "icu_outtime                  0\n",
       "aspirin                      0\n",
       "p2y12                        0\n",
       "other_antiplatelet           0\n",
       "starttime                    0\n",
       "stoptime                    42\n",
       "addmittime_dt                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45d7601d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16407"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subject_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e368dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaT\n",
       "1        NaT\n",
       "2        NaT\n",
       "3        NaT\n",
       "4        NaT\n",
       "          ..\n",
       "245333   NaT\n",
       "245334   NaT\n",
       "245335   NaT\n",
       "245336   NaT\n",
       "245337   NaT\n",
       "Name: deathtime, Length: 245338, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df['deathtime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0375ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245156</th>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245157</th>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245158</th>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245159</th>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245160</th>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91195 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              dod\n",
       "149     23/9/2154\n",
       "150     23/9/2154\n",
       "151     23/9/2154\n",
       "152     23/9/2154\n",
       "153     23/9/2154\n",
       "...           ...\n",
       "245156  20/2/2124\n",
       "245157  20/2/2124\n",
       "245158  20/2/2124\n",
       "245159  20/2/2124\n",
       "245160  20/2/2124\n",
       "\n",
       "[91195 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# makes no sense\n",
    "df[df['dod'].notna()][['dod']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8167973",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to datetime\n",
    "df['deathtime_dt'] = pd.to_datetime(df['deathtime'])\n",
    "df['charttime_dt'] = pd.to_datetime(df['charttime'])\n",
    "df['storetime_dt'] = pd.to_datetime(df['storetime'])\n",
    "df['icu_intime_dt'] = pd.to_datetime(df['icu_intime'])\n",
    "df['icu_outtime_dt'] = pd.to_datetime(df['icu_outtime'])\n",
    "df['starttime_dt'] = pd.to_datetime(df['starttime'])\n",
    "df['stoptime_dt'] = pd.to_datetime(df['stoptime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f7d8704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245338 entries, 0 to 245337\n",
      "Data columns (total 26 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   subject_id            245338 non-null  int64         \n",
      " 1   hospital_expire_flag  245338 non-null  int64         \n",
      " 2   deathtime             35338 non-null   object        \n",
      " 3   gender                245338 non-null  object        \n",
      " 4   anchor_age            245338 non-null  int64         \n",
      " 5   dod                   91195 non-null   object        \n",
      " 6   valuenum              245338 non-null  float64       \n",
      " 7   valueuom              245338 non-null  object        \n",
      " 8   itemid                245338 non-null  int64         \n",
      " 9   charttime             245338 non-null  object        \n",
      " 10  storetime             245338 non-null  object        \n",
      " 11  icu_intime            245338 non-null  object        \n",
      " 12  icu_outtime           245338 non-null  object        \n",
      " 13  aspirin               245338 non-null  int64         \n",
      " 14  p2y12                 245338 non-null  int64         \n",
      " 15  other_antiplatelet    245338 non-null  int64         \n",
      " 16  starttime             245338 non-null  object        \n",
      " 17  stoptime              245296 non-null  object        \n",
      " 18  addmittime_dt         245338 non-null  datetime64[ns]\n",
      " 19  deathtime_dt          35338 non-null   datetime64[ns]\n",
      " 20  charttime_dt          245338 non-null  datetime64[ns]\n",
      " 21  storetime_dt          245338 non-null  datetime64[ns]\n",
      " 22  icu_intime_dt         245338 non-null  datetime64[ns]\n",
      " 23  icu_outtime_dt        245338 non-null  datetime64[ns]\n",
      " 24  starttime_dt          245338 non-null  datetime64[ns]\n",
      " 25  stoptime_dt           245296 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](8), float64(1), int64(7), object(10)\n",
      "memory usage: 50.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# double check datetime\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8778a575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.626866\n",
       "F    0.373134\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique value\n",
    "df['gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e5f77f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dod to mortality if value exists\n",
    "df['mortality_dod'] = df['dod'].apply(lambda x: 0 if  x is np.nan else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ca0d823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert deathtime to mortality if value exists\n",
    "df['mortality_deathtime'] = df['deathtime'].apply(lambda x: 0 if  x is np.nan else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "56a3a448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality_dod</th>\n",
       "      <th>dod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1</td>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1</td>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1</td>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1</td>\n",
       "      <td>23/9/2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245156</th>\n",
       "      <td>1</td>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245157</th>\n",
       "      <td>1</td>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245158</th>\n",
       "      <td>1</td>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245159</th>\n",
       "      <td>1</td>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245160</th>\n",
       "      <td>1</td>\n",
       "      <td>20/2/2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mortality_dod        dod\n",
       "149                 1  23/9/2154\n",
       "150                 1  23/9/2154\n",
       "151                 1  23/9/2154\n",
       "152                 1  23/9/2154\n",
       "153                 1  23/9/2154\n",
       "...               ...        ...\n",
       "245156              1  20/2/2124\n",
       "245157              1  20/2/2124\n",
       "245158              1  20/2/2124\n",
       "245159              1  20/2/2124\n",
       "245160              1  20/2/2124\n",
       "\n",
       "[91195 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check\n",
    "df[df['mortality_dod']==1][['mortality_dod','dod']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f02e66a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality_dod</th>\n",
       "      <th>dod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245333</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245334</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245335</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245336</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245337</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154143 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mortality_dod  dod\n",
       "0                   0  NaN\n",
       "1                   0  NaN\n",
       "2                   0  NaN\n",
       "3                   0  NaN\n",
       "4                   0  NaN\n",
       "...               ...  ...\n",
       "245333              0  NaN\n",
       "245334              0  NaN\n",
       "245335              0  NaN\n",
       "245336              0  NaN\n",
       "245337              0  NaN\n",
       "\n",
       "[154143 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check\n",
    "df[df['mortality_dod']==0][['mortality_dod','dod']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "57444d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check predicted\n",
    "# df[df['mortality_deathtime']==1][['mortality_deathtime','deathtime_dt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2cc7723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore\n",
    "# df['mortality_dod'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a255dd86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['mortality_deathtime'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cccb85a2",
   "metadata": {},
   "source": [
    "### numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a660eb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_df = df.select_dtypes(include=['int64','float64']).copy()\n",
    "num_df = df[['anchor_age','valuenum']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "241555b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245338 entries, 0 to 245337\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   anchor_age  245338 non-null  int64  \n",
      " 1   valuenum    245338 non-null  float64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 5.6 MB\n"
     ]
    }
   ],
   "source": [
    "num_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8388bb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot overview distributions of numerical variables\n",
    "# determining number of columns\n",
    "ncols = 2\n",
    "cols = num_df.columns\n",
    "\n",
    "# determining number of rows\n",
    "nrows = round(int(np.ceil(len(num_df.columns)) / (1.0*ncols)))\n",
    "\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(15,50))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for col, ax in zip(cols, axes):\n",
    "    sns.histplot(data=num_df[col], kde=True, stat='density', ax=ax)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374d0fb8",
   "metadata": {},
   "source": [
    "**anchor_age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "05595480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD5CAYAAAA+0W6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAPa0lEQVR4nO3df6zdd13H8eeLdrgfwGi326Yy2NXkBia4lXFDBhMUyhZhSAtaxAjeaEONITKMCVzUZKDBlKjEHzHECjHXiEgHzDZbXFYvbrrEDW73i40CjbBVoLZ3c4zBCGz17R/3u+2uvZdzbnvPvf3sPh/Jyfecz/2e+31vWZ58+d7zI1WFJKk9z1juASRJJ8aAS1KjDLgkNcqAS1KjDLgkNWr1Uh7s3HPPreHh4aU8pCQ1b9++ffdX1dCx60sa8OHhYaamppbykJLUvCT3zbXuJRRJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RG9RXwJFcmuTvJPUne062tTbI3yYFuu2awo0r9S7IkN2k59Qx4kpcA7wReDlwEvDHJCDAOTFbVCDDZPZZOCVW1oNv577t2wc/xy1C03Po5A78AuKWqHqmqx4CbgDcDm4GJbp8JYMtgRpQkzaWfgN8NvDrJOUnOBN4APB9YX1WHALrturmenGR7kqkkU9PT04s1tySteD0DXlX7gQ8De4HrgTuBx/o9QFXtrKrRqhodGjruw7QkSSeorz9iVtXHq+riqno18L/AAeBwkg0A3fbI4MaUJB2r31ehrOu2LwDeAnwS2AOMdbuMAbsHMaAkaW79fh74Z5KcAzwKvKuqHkyyA9iVZBtwENg6qCElScfrK+BV9ao51h4ANi36RJKkvvhOTElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEb1+6XGv5PkniR3J/lkktOTrE2yN8mBbrtm0MNKkp7UM+BJnge8GxitqpcAq4C3AePAZFWNAJPdY0nSEun3Espq4Iwkq4EzgW8Bm4GJ7ucTwJbFH0+SNJ+eAa+qbwJ/ChwEDgEPVdUNwPqqOtTtcwhYN9fzk2xPMpVkanp6evEml6QVrp9LKGuYOdv+CeDHgbOSvL3fA1TVzqoararRoaGhE59UkvQU/VxCeR3w9aqarqpHgc8CrwQOJ9kA0G2PDG5MSdKx+gn4QeCSJGcmCbAJ2A/sAca6fcaA3YMZUZI0l9W9dqiqW5N8GrgNeAy4HdgJPAvYlWQbM5HfOshBJUlP1TPgAFV1FXDVMcs/YOZsXJK0DPoKuLScLvrgDTz0/UcHfpzh8esG+vvPPuM07rzq8oEeQyuLAdcp76HvP8q9O65Y7jFO2qD/B0Irj5+FIkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1KieAU/ywiR3zLp9J8l7kqxNsjfJgW67ZikGliTN6BnwqvpKVW2sqo3Ay4BHgGuAcWCyqkaAye6xJGmJLPQSyibgv6rqPmAzMNGtTwBbFnMwSdKPttCAvw34ZHd/fVUdAui26+Z6QpLtSaaSTE1PT5/4pJKkp+g74EmeCbwJuHohB6iqnVU1WlWjQ0NDC51PkjSPhZyBvx64raoOd48PJ9kA0G2PLPZwkqT5rV7Avr/Ck5dPAPYAY8CObrt7EeeSnvDsC8b56Yn2/0b+7AsArljuMfQ00lfAk5wJXAb85qzlHcCuJNuAg8DWxR9Pgof37+DeHe2Hb3j8uuUeQU8zfQW8qh4Bzjlm7QFmXpUiSVoGvhNTkhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUQZckhplwCWpUX0FPMlzk3w6yZeT7E/yiiRrk+xNcqDbrhn0sJKkJ/V7Bv4XwPVV9SLgImA/MA5MVtUIMNk9liQtkZ4BT/Ic4NXAxwGq6odV9W1gMzDR7TYBbBnUkJKk4/VzBv6TwDTwd0luT/KxJGcB66vqEEC3XTfXk5NsTzKVZGp6enrRBpekla6fgK8GLgY+WlUvBb7HAi6XVNXOqhqtqtGhoaETHFOSdKx+Av4N4BtVdWv3+NPMBP1wkg0A3fbIYEaUJM1lda8dqup/kvx3khdW1VeATcCXutsYsKPb7h7opFrRhsevW+4RTtrZZ5y23CPoaaZnwDu/DXwiyTOBrwG/zszZ+64k24CDwNbBjKiV7t4dVwz8GMPj1y3JcaTF1FfAq+oOYHSOH21a3HEkSf3ynZiS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmNMuCS1CgDLkmN6usr1ZLcCzwMHAUeq6rRJGuBTwHDwL3AW6vqwcGMKUk61kLOwF9TVRur6vHvxhwHJqtqBJjsHkuSlsjJXELZDEx09yeALSc/jiSpX/0GvIAbkuxLsr1bW19VhwC67bq5nphke5KpJFPT09MnP7EkCejzGjhwaVV9K8k6YG+SL/d7gKraCewEGB0drROYUZI0h77OwKvqW932CHAN8HLgcJINAN32yKCGlCQdr2fAk5yV5NmP3wcuB+4G9gBj3W5jwO5BDSlJOl4/l1DWA9ckeXz/f6yq65N8AdiVZBtwENg6uDElScfqGfCq+hpw0RzrDwCbBjGUJKk334kpSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUqL4DnmRVktuTXNs9Xptkb5ID3XbN4MaUJB1rIWfgVwL7Zz0eByaragSY7B5LkpZIXwFPch5wBfCxWcubgYnu/gSwZXFHkyT9KP2egf858F7g/2atra+qQwDddt1cT0yyPclUkqnp6emTGlaS9KSeAU/yRuBIVe07kQNU1c6qGq2q0aGhoRP5FZKkOazuY59LgTcleQNwOvCcJP8AHE6yoaoOJdkAHBnkoJKkp+p5Bl5V76+q86pqGHgb8LmqejuwBxjrdhsDdg9sSknScU7mdeA7gMuSHAAu6x5LkpZIP5dQnlBVNwI3dvcfADYt/kiSpH74TkxJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RGGXBJapQBl6RG9Qx4ktOTfD7JnUnuSfLBbn1tkr1JDnTbNYMfV5L0uH6+E/MHwGur6rtJTgNuTvIvwFuAyarakWQcGAfeN8BZpb4lWfhzPrzw41TVwp8kLZKeZ+A147vdw9O6WwGbgYlufQLYMpAJpRNQVT1vJ/t8463l1tc18CSrktwBHAH2VtWtwPqqOgTQbdcNbkxJ0rH6CnhVHa2qjcB5wMuTvKTfAyTZnmQqydT09PSJzikN1MjIyHKPIC3Ygl6FUlXfBm4Efh44nGQDQLc9Ms9zdlbVaFWNDg0NneS40uKrKr761a96SUTN6edVKENJntvdPwN4HfBlYA8w1u02Buwe1JDSICV54ia1pJ9XoWwAJpKsYib4u6rq2iT/CexKsg04CGwd4JySpGP0DHhV3QW8dI71B4BNgxhKWmojIyMcOHBguceQFsR3YkpgvNUkAy5JjTLgktQoAy5JjernVSjS09rs13/7UkK1xIBrxTPaapWXUCSpUQZckhplwCWpUQZckhplwCWpUQZckhrlywi14vk6cLXKgGvFM9pqlZdQJKlRBlySGmXAJalRBlwCLrzwwuUeQVqwfr7U+PlJ/i3J/iT3JLmyW1+bZG+SA912zeDHlQbjrrvuWu4RpAXr5wz8MeB3q+oC4BLgXUl+ChgHJqtqBJjsHkuSlkjPgFfVoaq6rbv/MLAfeB6wGZjodpsAtgxqSEnS8RZ0DTzJMDPfUH8rsL6qDsFM5IF1iz2cJGl+fQc8ybOAzwDvqarvLOB525NMJZmanp4+kRklSXPoK+BJTmMm3p+oqs92y4eTbOh+vgE4Mtdzq2pnVY1W1ejQ0NBizCwtqqp64ia1pJ9XoQT4OLC/qj4y60d7gLHu/hiwe/HHkwYvyRM3qSX9fBbKpcA7gC8muaNb+z1gB7AryTbgILB1MCNKkubSM+BVdTMw36nJpsUdR1oe559/Pvfdd99yjyEtiO/ElMB4q0l+nKxWPD8PXK3yDFwrXhKGhoaMt5pjwLVizT7zvv/+++dcl05lXkLRimas1TLPwCWpUQZckhplwCWpUQZckhplwCWpUVnKv8InmQZ8y5tORecC9/fcS1oe51fVcR/nuqQBl05VSaaqanS555AWwksoktQoAy5JjTLg0oydyz2AtFBeA5ekRnkGLkmNMuCS1CgDLkmNMuB62koynOTu5Z5DGhQDLs0jiZ+Xr1OaAdcpK8k/J9mX5J4k27u17yb5UJI7k9ySZH23vj7JNd36nUle2f2aVUn+tvsdNyQ5o9t/Y/f8u7rnrenWb0zyx0luAq6cZ65fSHJrktuT/OusGYaS7E1yW5K/SXJfknO7n709yeeT3NH9bNVg/+1pJTDgOpX9RlW9DBgF3p3kHOAs4Jaqugj4d+Cd3b5/CdzUrV8M3NOtjwB/XVUvBr4N/GK3/vfA+6rqQuCLwFWzjvvcqvrZqvqzeea6Gbikql4K/BPw3m79KuBzVXUxcA3wAoAkFwC/DFxaVRuBo8CvntC/EWkW/y+iTmXvTvLm7v7zmYnxD4Fru7V9wGXd/dcCvwZQVUeBh7qz6q9X1R2z9h9OcjYzkb6pW58Arp513E/1mOs84FNJNgDPBL7erf8M8OZuhuuTPNitbwJeBnyh++LkM4Ajvf/xpR/NgOuUlOTngNcBr6iqR5LcCJwOPFpPvvvsKL3/G/7BrPtHmYlnL9/r8fO/Aj5SVXu6OT/w+Njz7B9goqre38expb55CUWnqrOBB7t4vwi4pMf+k8BvASRZleQ58+1YVQ8BDyZ5Vbf0DuCm+fafZ7ZvdvfHZq3fDLy1m+FyYM2s2X4pybruZ2uTnL+A40lzMuA6VV0PrE5yF/BHwC099r8SeE2SLzJzqeTFPfYfA/6k+/0bgT9cwGwfAK5O8h889TPEPwhcnuQ24PXAIeDhqvoS8AfADd3x9gIbFnA8aU5+Foq0SJL8GHC0qh5L8grgo90fLaWB8Bq4tHheAOxK8gxm/tj6zh77SyfFM3BpHkl+H9h6zPLVVfWh5ZhHOpYBl6RG+UdMSWqUAZekRhlwSWqUAZekRv0/iJNl2YdzhX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column =['anchor_age'], grid = False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb325324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    245338.000000\n",
       "mean         67.736156\n",
       "std          13.455024\n",
       "min          18.000000\n",
       "25%          59.000000\n",
       "50%          69.000000\n",
       "75%          78.000000\n",
       "max          91.000000\n",
       "Name: anchor_age, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['anchor_age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e437c16c",
   "metadata": {},
   "source": [
    "**valuenum**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "02beac42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column =['valuenum'], grid = False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ebe58283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    245338.000000\n",
       "mean          8.850404\n",
       "std           3.434129\n",
       "min           0.010000\n",
       "25%           8.000000\n",
       "50%           9.400000\n",
       "75%          10.800000\n",
       "max          42.600000\n",
       "Name: valuenum, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['valuenum'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfcf3ab",
   "metadata": {},
   "source": [
    "### categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2fa6d387",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only categorical columns\n",
    "nom_df = df.select_dtypes(include='object').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "717617eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245338 entries, 0 to 245337\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   deathtime    35338 non-null   object\n",
      " 1   gender       245338 non-null  object\n",
      " 2   dod          91195 non-null   object\n",
      " 3   valueuom     245338 non-null  object\n",
      " 4   charttime    245338 non-null  object\n",
      " 5   storetime    245338 non-null  object\n",
      " 6   icu_intime   245338 non-null  object\n",
      " 7   icu_outtime  245338 non-null  object\n",
      " 8   starttime    245338 non-null  object\n",
      " 9   stoptime     245296 non-null  object\n",
      "dtypes: object(10)\n",
      "memory usage: 20.6+ MB\n"
     ]
    }
   ],
   "source": [
    "nom_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "444acb5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.626866\n",
       "F    0.373134\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nom_df['gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae7441a",
   "metadata": {},
   "source": [
    "### earliest time when itemid (drug)==1\n",
    "<br> itemid (aspirin/p2y12/other_antiplatelet)==1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabda8c6",
   "metadata": {},
   "source": [
    "##### aspirin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db23a89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16407"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of subjects\n",
    "df['subject_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29abe58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "starttime_dt   2189-06-27 11:00:00\n",
       "Name: 1197, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['subject_id']==10000980) & df['aspirin']==1][['starttime_dt']].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f69d3895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>aspirin</th>\n",
       "      <th>aspirin_starttime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>1</td>\n",
       "      <td>2189-06-27 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>1</td>\n",
       "      <td>2160-05-19 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
       "      <td>1</td>\n",
       "      <td>2129-06-13 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1</td>\n",
       "      <td>2141-05-23 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>1</td>\n",
       "      <td>2136-03-25 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10002760</td>\n",
       "      <td>1</td>\n",
       "      <td>2141-04-21 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10003502</td>\n",
       "      <td>1</td>\n",
       "      <td>2169-08-27 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10004422</td>\n",
       "      <td>1</td>\n",
       "      <td>2111-01-18 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10004457</td>\n",
       "      <td>1</td>\n",
       "      <td>2141-12-18 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10004733</td>\n",
       "      <td>1</td>\n",
       "      <td>2174-05-12 08:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id  aspirin   aspirin_starttime\n",
       "0    10000980        1 2189-06-27 11:00:00\n",
       "1    10002013        1 2160-05-19 10:00:00\n",
       "2    10002430        1 2129-06-13 04:00:00\n",
       "3    10002495        1 2141-05-23 08:00:00\n",
       "4    10002527        1 2136-03-25 10:00:00\n",
       "5    10002760        1 2141-04-21 10:00:00\n",
       "6    10003502        1 2169-08-27 04:00:00\n",
       "7    10004422        1 2111-01-18 10:00:00\n",
       "8    10004457        1 2141-12-18 10:00:00\n",
       "9    10004733        1 2174-05-12 08:00:00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aspirin_starttime_df = df[df['aspirin']==1].sort_values(by=['subject_id','starttime_dt']).groupby(by='subject_id').first().reset_index()[['subject_id','aspirin','starttime_dt']]\n",
    "aspirin_starttime_df.rename(columns={'starttime_dt': 'aspirin_starttime'},inplace=True) \n",
    "aspirin_starttime_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4274138a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15818, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aspirin_starttime_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f03abc6",
   "metadata": {},
   "source": [
    "##### p2y12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0e599711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "starttime_dt   2189-06-27 11:00:00\n",
       "Name: 1196, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['subject_id']==10000980) & df['p2y12']==1][['starttime_dt']].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aafc6e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>p2y12</th>\n",
       "      <th>p2y12_starttime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>1</td>\n",
       "      <td>2189-06-27 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>1</td>\n",
       "      <td>2160-05-19 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1</td>\n",
       "      <td>2141-05-27 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10008077</td>\n",
       "      <td>1</td>\n",
       "      <td>2191-06-03 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10013310</td>\n",
       "      <td>1</td>\n",
       "      <td>2153-07-05 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10013643</td>\n",
       "      <td>1</td>\n",
       "      <td>2200-02-10 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10015931</td>\n",
       "      <td>1</td>\n",
       "      <td>2176-11-20 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10019385</td>\n",
       "      <td>1</td>\n",
       "      <td>2180-02-22 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10022281</td>\n",
       "      <td>1</td>\n",
       "      <td>2125-06-17 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10023771</td>\n",
       "      <td>1</td>\n",
       "      <td>2113-08-27 10:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id  p2y12     p2y12_starttime\n",
       "0    10000980      1 2189-06-27 11:00:00\n",
       "1    10002013      1 2160-05-19 10:00:00\n",
       "2    10002495      1 2141-05-27 06:00:00\n",
       "3    10008077      1 2191-06-03 08:00:00\n",
       "4    10013310      1 2153-07-05 08:00:00\n",
       "5    10013643      1 2200-02-10 08:00:00\n",
       "6    10015931      1 2176-11-20 08:00:00\n",
       "7    10019385      1 2180-02-22 08:00:00\n",
       "8    10022281      1 2125-06-17 10:00:00\n",
       "9    10023771      1 2113-08-27 10:00:00"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2y12_starttime_df = df[df['p2y12']==1].sort_values(by=['subject_id','starttime_dt']).groupby(by='subject_id').first().reset_index()[['subject_id','p2y12','starttime_dt']]\n",
    "p2y12_starttime_df.rename(columns={'starttime_dt': 'p2y12_starttime'},inplace=True) \n",
    "p2y12_starttime_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c917a4",
   "metadata": {},
   "source": [
    "##### other_antiplatelet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "497d9a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "starttime_dt   2163-04-25 13:00:00\n",
       "Name: 0, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['subject_id']==10881690) & df['other_antiplatelet']==1][['starttime_dt']].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2dcd942d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>other_antiplatelet</th>\n",
       "      <th>antiplatelet_starttime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10881690</td>\n",
       "      <td>1</td>\n",
       "      <td>2163-04-25 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12588030</td>\n",
       "      <td>1</td>\n",
       "      <td>2129-10-12 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12662064</td>\n",
       "      <td>1</td>\n",
       "      <td>2112-04-01 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12859888</td>\n",
       "      <td>1</td>\n",
       "      <td>2121-01-03 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13660560</td>\n",
       "      <td>1</td>\n",
       "      <td>2154-09-22 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13993123</td>\n",
       "      <td>1</td>\n",
       "      <td>2146-07-25 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14391747</td>\n",
       "      <td>1</td>\n",
       "      <td>2155-10-11 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14837844</td>\n",
       "      <td>1</td>\n",
       "      <td>2119-10-21 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15904250</td>\n",
       "      <td>1</td>\n",
       "      <td>2123-10-10 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16644308</td>\n",
       "      <td>1</td>\n",
       "      <td>2189-03-29 16:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id  other_antiplatelet antiplatelet_starttime\n",
       "0    10881690                   1    2163-04-25 12:00:00\n",
       "1    12588030                   1    2129-10-12 20:00:00\n",
       "2    12662064                   1    2112-04-01 20:00:00\n",
       "3    12859888                   1    2121-01-03 06:00:00\n",
       "4    13660560                   1    2154-09-22 18:00:00\n",
       "5    13993123                   1    2146-07-25 20:00:00\n",
       "6    14391747                   1    2155-10-11 20:00:00\n",
       "7    14837844                   1    2119-10-21 21:00:00\n",
       "8    15904250                   1    2123-10-10 08:00:00\n",
       "9    16644308                   1    2189-03-29 16:00:00"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_antiplatelet_starttime_df = df[df['other_antiplatelet']==1].sort_values(by=['subject_id','starttime_dt']).groupby(by='subject_id').first().reset_index()[['subject_id','other_antiplatelet','starttime_dt']]\n",
    "other_antiplatelet_starttime_df.rename(columns={'starttime_dt': 'antiplatelet_starttime'},inplace=True) \n",
    "other_antiplatelet_starttime_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6be61a",
   "metadata": {},
   "source": [
    "### time of medication from hosp_admittime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "03a135a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16407"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['subject_id','addmittime_dt']]['subject_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d9d234bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [aspirin_starttime_df, p2y12_starttime_df, other_antiplatelet_starttime_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e86db9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>aspirin</th>\n",
       "      <th>aspirin_starttime</th>\n",
       "      <th>p2y12</th>\n",
       "      <th>p2y12_starttime</th>\n",
       "      <th>other_antiplatelet</th>\n",
       "      <th>antiplatelet_starttime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2189-06-27 11:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2189-06-27 11:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2160-05-19 10:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2160-05-19 10:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2129-06-13 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2141-05-23 08:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2141-05-27 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2136-03-25 10:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16402</th>\n",
       "      <td>19985683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2131-07-10 10:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16403</th>\n",
       "      <td>12662064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2112-04-01 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16404</th>\n",
       "      <td>13660560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2154-09-22 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>14391747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2155-10-11 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16406</th>\n",
       "      <td>16870733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2193-05-28 20:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16407 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id  aspirin   aspirin_starttime  p2y12     p2y12_starttime  \\\n",
       "0        10000980      1.0 2189-06-27 11:00:00    1.0 2189-06-27 11:00:00   \n",
       "1        10002013      1.0 2160-05-19 10:00:00    1.0 2160-05-19 10:00:00   \n",
       "2        10002430      1.0 2129-06-13 04:00:00    NaN                 NaT   \n",
       "3        10002495      1.0 2141-05-23 08:00:00    1.0 2141-05-27 06:00:00   \n",
       "4        10002527      1.0 2136-03-25 10:00:00    NaN                 NaT   \n",
       "...           ...      ...                 ...    ...                 ...   \n",
       "16402    19985683      NaN                 NaT    1.0 2131-07-10 10:00:00   \n",
       "16403    12662064      NaN                 NaT    NaN                 NaT   \n",
       "16404    13660560      NaN                 NaT    NaN                 NaT   \n",
       "16405    14391747      NaN                 NaT    NaN                 NaT   \n",
       "16406    16870733      NaN                 NaT    NaN                 NaT   \n",
       "\n",
       "       other_antiplatelet antiplatelet_starttime  \n",
       "0                     NaN                    NaT  \n",
       "1                     NaN                    NaT  \n",
       "2                     NaN                    NaT  \n",
       "3                     NaN                    NaT  \n",
       "4                     NaN                    NaT  \n",
       "...                   ...                    ...  \n",
       "16402                 NaN                    NaT  \n",
       "16403                 1.0    2112-04-01 20:00:00  \n",
       "16404                 1.0    2154-09-22 18:00:00  \n",
       "16405                 1.0    2155-10-11 20:00:00  \n",
       "16406                 1.0    2193-05-28 20:00:00  \n",
       "\n",
       "[16407 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starttimes_df = ft.reduce(lambda left, right: pd.merge(left, right, on='subject_id', how='outer'), dfs)\n",
    "starttimes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fc67e934",
   "metadata": {},
   "outputs": [],
   "source": [
    "starttimes_df = starttimes_df.merge(addmittime_df,on='subject_id',how='inner').drop(columns=['aspirin','p2y12','other_antiplatelet'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8bc16350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>aspirin_starttime</th>\n",
       "      <th>p2y12_starttime</th>\n",
       "      <th>antiplatelet_starttime</th>\n",
       "      <th>addmittime_dt</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2189-06-27 11:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2189-06-27 07:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-19 10:00:00</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>2129-06-13 00:00:00</td>\n",
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       "      <td>10002495</td>\n",
       "      <td>2141-05-23 08:00:00</td>\n",
       "      <td>2141-05-27 06:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-05-22 20:17:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>2136-03-25 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2136-03-24 12:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16402</th>\n",
       "      <td>19985683</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2131-07-10 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2131-10-06 20:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16403</th>\n",
       "      <td>12662064</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2112-04-01 20:00:00</td>\n",
       "      <td>2112-01-04 16:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16404</th>\n",
       "      <td>13660560</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2154-09-22 18:00:00</td>\n",
       "      <td>2154-09-22 15:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>14391747</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2155-10-11 20:00:00</td>\n",
       "      <td>2155-11-10 19:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16406</th>\n",
       "      <td>16870733</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2193-05-28 20:00:00</td>\n",
       "      <td>2193-05-28 15:39:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16407 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id   aspirin_starttime     p2y12_starttime  \\\n",
       "0        10000980 2189-06-27 11:00:00 2189-06-27 11:00:00   \n",
       "1        10002013 2160-05-19 10:00:00 2160-05-19 10:00:00   \n",
       "2        10002430 2129-06-13 04:00:00                 NaT   \n",
       "3        10002495 2141-05-23 08:00:00 2141-05-27 06:00:00   \n",
       "4        10002527 2136-03-25 10:00:00                 NaT   \n",
       "...           ...                 ...                 ...   \n",
       "16402    19985683                 NaT 2131-07-10 10:00:00   \n",
       "16403    12662064                 NaT                 NaT   \n",
       "16404    13660560                 NaT                 NaT   \n",
       "16405    14391747                 NaT                 NaT   \n",
       "16406    16870733                 NaT                 NaT   \n",
       "\n",
       "      antiplatelet_starttime       addmittime_dt  \n",
       "0                        NaT 2189-06-27 07:38:00  \n",
       "1                        NaT 2160-05-18 07:45:00  \n",
       "2                        NaT 2129-06-13 00:00:00  \n",
       "3                        NaT 2141-05-22 20:17:00  \n",
       "4                        NaT 2136-03-24 12:30:00  \n",
       "...                      ...                 ...  \n",
       "16402                    NaT 2131-10-06 20:55:00  \n",
       "16403    2112-04-01 20:00:00 2112-01-04 16:18:00  \n",
       "16404    2154-09-22 18:00:00 2154-09-22 15:04:00  \n",
       "16405    2155-10-11 20:00:00 2155-11-10 19:48:00  \n",
       "16406    2193-05-28 20:00:00 2193-05-28 15:39:00  \n",
       "\n",
       "[16407 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starttimes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b9771ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16407 entries, 0 to 16406\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   subject_id              16407 non-null  int64         \n",
      " 1   aspirin_starttime       15818 non-null  datetime64[ns]\n",
      " 2   p2y12_starttime         3293 non-null   datetime64[ns]\n",
      " 3   antiplatelet_starttime  22 non-null     datetime64[ns]\n",
      " 4   addmittime_dt           16407 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](4), int64(1)\n",
      "memory usage: 769.1 KB\n"
     ]
    }
   ],
   "source": [
    "starttimes_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "70347cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time difference in hours\n",
    "starttimes_df['aspirin_timediff'] = (starttimes_df['aspirin_starttime'] - starttimes_df['addmittime_dt']).dt.seconds.div(3600)\n",
    "starttimes_df['p2y12_timediff'] = (starttimes_df['p2y12_starttime'] - starttimes_df['addmittime_dt']).dt.seconds.div(3600)\n",
    "starttimes_df['antiplatelet_timediff'] = (starttimes_df['antiplatelet_starttime'] - starttimes_df['addmittime_dt']).dt.seconds.div(3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "50f4792f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>subject_id</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>NaT</td>\n",
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       "      <td>2.250000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
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       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2129-06-13 00:00:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2141-05-27 06:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-05-22 20:17:00</td>\n",
       "      <td>11.716667</td>\n",
       "      <td>9.716667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>2136-03-25 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2136-03-24 12:30:00</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>2141-04-21 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-04-20 07:15:00</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10003502</td>\n",
       "      <td>2169-08-27 04:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2169-08-26 16:14:00</td>\n",
       "      <td>11.766667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10004422</td>\n",
       "      <td>2111-01-18 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2111-01-15 14:55:00</td>\n",
       "      <td>19.083333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10004457</td>\n",
       "      <td>2141-12-18 10:00:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2141-12-17 11:00:00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2174-12-04 11:28:00</td>\n",
       "      <td>20.533333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   aspirin_starttime     p2y12_starttime antiplatelet_starttime  \\\n",
       "0    10000980 2189-06-27 11:00:00 2189-06-27 11:00:00                    NaT   \n",
       "1    10002013 2160-05-19 10:00:00 2160-05-19 10:00:00                    NaT   \n",
       "2    10002430 2129-06-13 04:00:00                 NaT                    NaT   \n",
       "3    10002495 2141-05-23 08:00:00 2141-05-27 06:00:00                    NaT   \n",
       "4    10002527 2136-03-25 10:00:00                 NaT                    NaT   \n",
       "5    10002760 2141-04-21 10:00:00                 NaT                    NaT   \n",
       "6    10003502 2169-08-27 04:00:00                 NaT                    NaT   \n",
       "7    10004422 2111-01-18 10:00:00                 NaT                    NaT   \n",
       "8    10004457 2141-12-18 10:00:00                 NaT                    NaT   \n",
       "9    10004733 2174-05-12 08:00:00                 NaT                    NaT   \n",
       "\n",
       "        addmittime_dt  aspirin_timediff  p2y12_timediff  antiplatelet_timediff  \n",
       "0 2189-06-27 07:38:00          3.366667        3.366667                    NaN  \n",
       "1 2160-05-18 07:45:00          2.250000        2.250000                    NaN  \n",
       "2 2129-06-13 00:00:00          4.000000             NaN                    NaN  \n",
       "3 2141-05-22 20:17:00         11.716667        9.716667                    NaN  \n",
       "4 2136-03-24 12:30:00         21.500000             NaN                    NaN  \n",
       "5 2141-04-20 07:15:00          2.750000             NaN                    NaN  \n",
       "6 2169-08-26 16:14:00         11.766667             NaN                    NaN  \n",
       "7 2111-01-15 14:55:00         19.083333             NaN                    NaN  \n",
       "8 2141-12-17 11:00:00         23.000000             NaN                    NaN  \n",
       "9 2174-12-04 11:28:00         20.533333             NaN                    NaN  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starttimes_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0caf445b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# aspirin_starttime_df\n",
    "# p2y12_starttime_df\n",
    "# other_antiplatelet_starttime_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "395c3757",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>addmittime_dt</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>...</th>\n",
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       "      <th>245333</th>\n",
       "      <td>19997752</td>\n",
       "      <td>2128-02-28 21:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245334</th>\n",
       "      <td>19997752</td>\n",
       "      <td>2128-02-28 21:28:00</td>\n",
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       "    <tr>\n",
       "      <th>245335</th>\n",
       "      <td>19997752</td>\n",
       "      <td>2128-02-28 21:28:00</td>\n",
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       "    <tr>\n",
       "      <th>245336</th>\n",
       "      <td>19997752</td>\n",
       "      <td>2128-02-28 21:28:00</td>\n",
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       "    <tr>\n",
       "      <th>245337</th>\n",
       "      <td>19997752</td>\n",
       "      <td>2128-02-28 21:28:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>245338 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        subject_id       addmittime_dt\n",
       "0         10881690 2163-04-25 10:41:00\n",
       "1         10881690 2163-04-25 10:41:00\n",
       "2         10881690 2163-04-25 10:41:00\n",
       "3         10881690 2163-04-25 10:41:00\n",
       "4         10881690 2163-04-25 10:41:00\n",
       "...            ...                 ...\n",
       "245333    19997752 2128-02-28 21:28:00\n",
       "245334    19997752 2128-02-28 21:28:00\n",
       "245335    19997752 2128-02-28 21:28:00\n",
       "245336    19997752 2128-02-28 21:28:00\n",
       "245337    19997752 2128-02-28 21:28:00\n",
       "\n",
       "[245338 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['subject_id','addmittime_dt']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463e018e",
   "metadata": {},
   "source": [
    "### earliest time itemid (troponin) consecutive > 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4257ecd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51222, 51003], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['itemid'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8dfe99af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>valuenum</th>\n",
       "      <th>storetime_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>12588030</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2129-10-12 14:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>12588030</td>\n",
       "      <td>6.39</td>\n",
       "      <td>2129-10-12 21:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>12588030</td>\n",
       "      <td>7.51</td>\n",
       "      <td>2129-11-12 05:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12588030</td>\n",
       "      <td>8.78</td>\n",
       "      <td>2129-12-13 07:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject_id  valuenum        storetime_dt\n",
       "90    12588030      4.42 2129-10-12 14:11:00\n",
       "92    12588030      6.39 2129-10-12 21:19:00\n",
       "93    12588030      7.51 2129-11-12 05:06:00\n",
       "97    12588030      8.78 2129-12-13 07:04:00"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['subject_id']==12588030) & (df['itemid']==51003)][['subject_id','valuenum','storetime_dt']].sort_values(by=['subject_id','storetime_dt']).drop_duplicates(subset=['subject_id','storetime_dt'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f01fc9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>valuenum</th>\n",
       "      <th>storetime_dt</th>\n",
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       "      <th>1196</th>\n",
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       "      <td>2189-06-27 14:18:00</td>\n",
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       "      <th>1210</th>\n",
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       "      <td>2141-05-22 23:05:00</td>\n",
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       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>10002495</td>\n",
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       "      <td>2141-05-23 02:35:00</td>\n",
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       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>10002495</td>\n",
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       "      <td>2141-05-23 08:02:00</td>\n",
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       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>10002495</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2141-05-23 12:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>10002495</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2141-05-23 18:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>10002495</td>\n",
       "      <td>6.74</td>\n",
       "      <td>2141-05-24 08:23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80931</th>\n",
       "      <td>10003502</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2169-08-27 07:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80999</th>\n",
       "      <td>10007058</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2167-08-11 01:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81001</th>\n",
       "      <td>10007058</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2167-08-11 07:29:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id  valuenum        storetime_dt\n",
       "1196     10000980      0.11 2189-06-27 14:18:00\n",
       "1210     10002495      0.69 2141-05-22 23:05:00\n",
       "1212     10002495      0.84 2141-05-23 02:35:00\n",
       "1213     10002495      1.75 2141-05-23 08:02:00\n",
       "1215     10002495      3.11 2141-05-23 12:34:00\n",
       "1217     10002495      3.75 2141-05-23 18:25:00\n",
       "1220     10002495      6.74 2141-05-24 08:23:00\n",
       "80931    10003502      0.13 2169-08-27 07:38:00\n",
       "80999    10007058      0.70 2167-08-11 01:18:00\n",
       "81001    10007058      1.37 2167-08-11 07:29:00"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop_inc_df = df[(df['itemid']==51003)][['subject_id','valuenum','storetime_dt']].sort_values(by=['subject_id','storetime_dt']).drop_duplicates(subset=['subject_id','storetime_dt'],keep='first')\n",
    "trop_inc_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "abfe819a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time_diff convert to hrs\n",
    "trop_inc_df['storetime_diff'] = trop_inc_df['storetime_dt'].diff().dt.seconds.div(3600)\n",
    "\n",
    "# troponin diff in pct_change\n",
    "trop_inc_df['trop_pct'] = trop_inc_df['valuenum'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f6020289",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>valuenum</th>\n",
       "      <th>storetime_dt</th>\n",
       "      <th>storetime_diff</th>\n",
       "      <th>trop_pct</th>\n",
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       "  </thead>\n",
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       "      <th>1196</th>\n",
       "      <td>10000980</td>\n",
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       "      <td>2189-06-27 14:18:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>10002495</td>\n",
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       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>10002495</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2141-05-23 02:35:00</td>\n",
       "      <td>3.500000</td>\n",
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       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2141-05-23 08:02:00</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>1.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>10002495</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2141-05-23 12:34:00</td>\n",
       "      <td>4.533333</td>\n",
       "      <td>0.777143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject_id  valuenum        storetime_dt  storetime_diff  trop_pct\n",
       "1196    10000980      0.11 2189-06-27 14:18:00             NaN       NaN\n",
       "1210    10002495      0.69 2141-05-22 23:05:00        8.783333  5.272727\n",
       "1212    10002495      0.84 2141-05-23 02:35:00        3.500000  0.217391\n",
       "1213    10002495      1.75 2141-05-23 08:02:00        5.450000  1.083333\n",
       "1215    10002495      3.11 2141-05-23 12:34:00        4.533333  0.777143"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop_inc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4647823b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15245 entries, 1196 to 245318\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   subject_id      15245 non-null  int64         \n",
      " 1   valuenum        15245 non-null  float64       \n",
      " 2   storetime_dt    15245 non-null  datetime64[ns]\n",
      " 3   storetime_diff  15244 non-null  float64       \n",
      " 4   trop_pct        15244 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1)\n",
      "memory usage: 714.6 KB\n"
     ]
    }
   ],
   "source": [
    "trop_inc_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3b0d2a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1210</th>\n",
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       "      <td>8.783333</td>\n",
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       "      <th>1212</th>\n",
       "      <td>10002495</td>\n",
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       "      <th>1213</th>\n",
       "      <td>10002495</td>\n",
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       "      <th>1215</th>\n",
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       "      <td>3.11</td>\n",
       "      <td>2141-05-23 12:34:00</td>\n",
       "      <td>4.533333</td>\n",
       "      <td>0.777143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>10002495</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2141-05-23 18:25:00</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>0.205788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245155</th>\n",
       "      <td>19997293</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2123-10-14 18:09:00</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>-0.836735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245157</th>\n",
       "      <td>19997293</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2123-10-15 03:08:00</td>\n",
       "      <td>8.983333</td>\n",
       "      <td>-0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80881</th>\n",
       "      <td>19997473</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2173-09-14 07:09:00</td>\n",
       "      <td>7.050000</td>\n",
       "      <td>-0.012821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245324</th>\n",
       "      <td>19997752</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245320</th>\n",
       "      <td>19997752</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2128-04-03 14:03:00</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>-0.263158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10890 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        subject_id  valuenum        storetime_dt  storetime_diff  trop_pct\n",
       "1210      10002495      0.69 2141-05-22 23:05:00        8.783333  5.272727\n",
       "1212      10002495      0.84 2141-05-23 02:35:00        3.500000  0.217391\n",
       "1213      10002495      1.75 2141-05-23 08:02:00        5.450000  1.083333\n",
       "1215      10002495      3.11 2141-05-23 12:34:00        4.533333  0.777143\n",
       "1217      10002495      3.75 2141-05-23 18:25:00        5.850000  0.205788\n",
       "...            ...       ...                 ...             ...       ...\n",
       "245155    19997293      0.08 2123-10-14 18:09:00        6.600000 -0.836735\n",
       "245157    19997293      0.07 2123-10-15 03:08:00        8.983333 -0.125000\n",
       "80881     19997473      3.08 2173-09-14 07:09:00        7.050000 -0.012821\n",
       "245324    19997752      0.38 2128-03-03 10:09:00        3.000000 -0.876623\n",
       "245320    19997752      0.28 2128-04-03 14:03:00        3.900000 -0.263158\n",
       "\n",
       "[10890 rows x 5 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop_inc_df[trop_inc_df['storetime_diff'] <= (12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "466c244b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>level_1</th>\n",
       "      <th>valuenum</th>\n",
       "      <th>storetime_dt</th>\n",
       "      <th>storetime_diff</th>\n",
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       "      <th>0</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1212</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2141-05-23 02:35:00</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1213</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2141-05-23 08:02:00</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>1.083333</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1215</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2141-05-23 12:34:00</td>\n",
       "      <td>4.533333</td>\n",
       "      <td>0.777143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1217</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2141-05-23 18:25:00</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>0.205788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002495</td>\n",
       "      <td>1220</td>\n",
       "      <td>6.74</td>\n",
       "      <td>2141-05-24 08:23:00</td>\n",
       "      <td>13.966667</td>\n",
       "      <td>0.797333</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>10093</th>\n",
       "      <td>19993336</td>\n",
       "      <td>244963</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2171-07-09 12:17:00</td>\n",
       "      <td>6.466667</td>\n",
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       "    <tr>\n",
       "      <th>10094</th>\n",
       "      <td>19997293</td>\n",
       "      <td>245157</td>\n",
       "      <td>0.07</td>\n",
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       "      <td>8.983333</td>\n",
       "      <td>-0.125000</td>\n",
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       "    <tr>\n",
       "      <th>10095</th>\n",
       "      <td>19997473</td>\n",
       "      <td>80881</td>\n",
       "      <td>3.08</td>\n",
       "      <td>2173-09-14 07:09:00</td>\n",
       "      <td>7.050000</td>\n",
       "      <td>-0.012821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10096</th>\n",
       "      <td>19997752</td>\n",
       "      <td>245320</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2128-04-03 14:03:00</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>-0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10097</th>\n",
       "      <td>19997752</td>\n",
       "      <td>245318</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2128-05-03 11:28:00</td>\n",
       "      <td>21.416667</td>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10098 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id  level_1  valuenum        storetime_dt  storetime_diff  \\\n",
       "0        10002495     1212      0.84 2141-05-23 02:35:00        3.500000   \n",
       "1        10002495     1213      1.75 2141-05-23 08:02:00        5.450000   \n",
       "2        10002495     1215      3.11 2141-05-23 12:34:00        4.533333   \n",
       "3        10002495     1217      3.75 2141-05-23 18:25:00        5.850000   \n",
       "4        10002495     1220      6.74 2141-05-24 08:23:00       13.966667   \n",
       "...           ...      ...       ...                 ...             ...   \n",
       "10093    19993336   244963      1.00 2171-07-09 12:17:00        6.466667   \n",
       "10094    19997293   245157      0.07 2123-10-15 03:08:00        8.983333   \n",
       "10095    19997473    80881      3.08 2173-09-14 07:09:00        7.050000   \n",
       "10096    19997752   245320      0.28 2128-04-03 14:03:00        3.900000   \n",
       "10097    19997752   245318      0.50 2128-05-03 11:28:00       21.416667   \n",
       "\n",
       "       trop_pct  \n",
       "0      0.217391  \n",
       "1      1.083333  \n",
       "2      0.777143  \n",
       "3      0.205788  \n",
       "4      0.797333  \n",
       "...         ...  \n",
       "10093 -0.107143  \n",
       "10094 -0.125000  \n",
       "10095 -0.012821  \n",
       "10096 -0.263158  \n",
       "10097  0.785714  \n",
       "\n",
       "[10098 rows x 6 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop first row of each subject\n",
    "trop_inc_df.groupby('subject_id').apply(lambda group: group.iloc[1:, 1:]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "717f027b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>subject_id</th>\n",
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       "      <td>10000980</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002495</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003502</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2169-08-27 07:38:00</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>-0.980712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10007058</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2167-08-11 01:18:00</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>4.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10008077</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2191-05-03 23:03:00</td>\n",
       "      <td>16.150000</td>\n",
       "      <td>-0.924188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142</th>\n",
       "      <td>19994505</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2185-03-11 18:30:00</td>\n",
       "      <td>11.833333</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5143</th>\n",
       "      <td>19995780</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2125-10-20 11:33:00</td>\n",
       "      <td>17.050000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5144</th>\n",
       "      <td>19997293</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2123-10-14 18:09:00</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>-0.836735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5145</th>\n",
       "      <td>19997473</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2173-09-14 00:06:00</td>\n",
       "      <td>20.966667</td>\n",
       "      <td>43.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5146</th>\n",
       "      <td>19997752</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5147 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject_id  valuenum       trop_risetime  storetime_diff   trop_pct\n",
       "0       10000980      0.11 2189-06-27 14:18:00             NaN        NaN\n",
       "1       10002495      0.69 2141-05-22 23:05:00        8.783333   5.272727\n",
       "2       10003502      0.13 2169-08-27 07:38:00       23.250000  -0.980712\n",
       "3       10007058      0.70 2167-08-11 01:18:00       17.666667   4.384615\n",
       "4       10008077      0.21 2191-05-03 23:03:00       16.150000  -0.924188\n",
       "...          ...       ...                 ...             ...        ...\n",
       "5142    19994505      0.01 2185-03-11 18:30:00       11.833333  -0.500000\n",
       "5143    19995780      0.49 2125-10-20 11:33:00       17.050000  48.000000\n",
       "5144    19997293      0.08 2123-10-14 18:09:00        6.600000  -0.836735\n",
       "5145    19997473      3.12 2173-09-14 00:06:00       20.966667  43.571429\n",
       "5146    19997752      0.38 2128-03-03 10:09:00        3.000000  -0.876623\n",
       "\n",
       "[5147 rows x 5 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop_inc_starttime = trop_inc_df.groupby('subject_id').first().reset_index()\n",
    "trop_inc_starttime.rename(columns={'storetime_dt': 'trop_risetime'},inplace=True) \n",
    "trop_inc_starttime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59a8722",
   "metadata": {},
   "source": [
    "### time closest BEFORE trop_time , itemid (hemoglobin)==1\n",
    "itemid = 51002 OR itemid = 51003 OR itemid = 52642 (troponin)\n",
    "<br> itemid = 51222 (hemoglobin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8b145786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hemo_charttime_df = df[df['itemid']==51222].sort_values(by=['subject_id','storetime_dt']).groupby(by='subject_id').first().reset_index()[['subject_id','itemid','storetime_dt']]\n",
    "# hemo_charttime_df.rename(columns={'storetime_dt': 'hemo_storetime'},inplace=True) \n",
    "# hemo_charttime_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "199d7fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[(df['itemid']==51222) & (df['valuenum']>=7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "069b168d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>valuenum</th>\n",
       "      <th>hemo_storetime</th>\n",
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       "      <th>1198</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 13:16:00</td>\n",
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       "      <th>1201</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 13:16:00</td>\n",
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       "      <th>1204</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 13:16:00</td>\n",
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       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 13:16:00</td>\n",
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       "      <th>1199</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 15:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>10002013</td>\n",
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       "      <td>2160-05-18 15:07:00</td>\n",
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       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>10002013</td>\n",
       "      <td>51222</td>\n",
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       "      <td>2160-05-18 15:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>10002013</td>\n",
       "      <td>51222</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2160-05-18 15:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>10002013</td>\n",
       "      <td>51222</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2160-05-19 04:17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>10002013</td>\n",
       "      <td>51222</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2160-05-19 04:17:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject_id  itemid  valuenum      hemo_storetime\n",
       "1198    10002013   51222      10.2 2160-05-18 13:16:00\n",
       "1201    10002013   51222      10.2 2160-05-18 13:16:00\n",
       "1204    10002013   51222      10.2 2160-05-18 13:16:00\n",
       "1207    10002013   51222      10.2 2160-05-18 13:16:00\n",
       "1199    10002013   51222      11.2 2160-05-18 15:07:00\n",
       "1202    10002013   51222      11.2 2160-05-18 15:07:00\n",
       "1205    10002013   51222      11.2 2160-05-18 15:07:00\n",
       "1208    10002013   51222      11.2 2160-05-18 15:07:00\n",
       "1200    10002013   51222      10.9 2160-05-19 04:17:00\n",
       "1203    10002013   51222      10.9 2160-05-19 04:17:00"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo = df[(df['itemid']==51222) & (df['valuenum']>=7)].sort_values(by=['subject_id','storetime_dt'])[['subject_id','itemid','valuenum','storetime_dt']].rename(columns={'storetime_dt': 'hemo_storetime'})\n",
    "tmp_hemo.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b2d68e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208268, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "27b43faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>itemid</th>\n",
       "      <th>valuenum_x</th>\n",
       "      <th>hemo_storetime</th>\n",
       "      <th>valuenum_y</th>\n",
       "      <th>trop_risetime</th>\n",
       "      <th>storetime_diff</th>\n",
       "      <th>trop_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10002495</td>\n",
       "      <td>51222</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2141-05-22 22:22:00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002495</td>\n",
       "      <td>51222</td>\n",
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       "      <td>2141-05-22 22:22:00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002495</td>\n",
       "      <td>51222</td>\n",
       "      <td>14.3</td>\n",
       "      <td>2141-05-23 08:06:00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>51222</td>\n",
       "      <td>14.3</td>\n",
       "      <td>2141-05-23 08:06:00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002495</td>\n",
       "      <td>51222</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2141-05-23 15:28:00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>5.272727</td>\n",
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       "      <th>...</th>\n",
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       "      <th>89036</th>\n",
       "      <td>19997752</td>\n",
       "      <td>51222</td>\n",
       "      <td>9.1</td>\n",
       "      <td>2128-04-03 03:24:00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89037</th>\n",
       "      <td>19997752</td>\n",
       "      <td>51222</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2128-06-03 04:01:00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89038</th>\n",
       "      <td>19997752</td>\n",
       "      <td>51222</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2128-06-03 04:01:00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89039</th>\n",
       "      <td>19997752</td>\n",
       "      <td>51222</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2128-07-03 03:29:00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89040</th>\n",
       "      <td>19997752</td>\n",
       "      <td>51222</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2128-07-03 03:29:00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2128-03-03 10:09:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.876623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89041 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id  itemid  valuenum_x      hemo_storetime  valuenum_y  \\\n",
       "0        10002495   51222        14.0 2141-05-22 22:22:00        0.69   \n",
       "1        10002495   51222        14.0 2141-05-22 22:22:00        0.69   \n",
       "2        10002495   51222        14.3 2141-05-23 08:06:00        0.69   \n",
       "3        10002495   51222        14.3 2141-05-23 08:06:00        0.69   \n",
       "4        10002495   51222        13.0 2141-05-23 15:28:00        0.69   \n",
       "...           ...     ...         ...                 ...         ...   \n",
       "89036    19997752   51222         9.1 2128-04-03 03:24:00        0.38   \n",
       "89037    19997752   51222         8.4 2128-06-03 04:01:00        0.38   \n",
       "89038    19997752   51222         8.4 2128-06-03 04:01:00        0.38   \n",
       "89039    19997752   51222         8.8 2128-07-03 03:29:00        0.38   \n",
       "89040    19997752   51222         8.8 2128-07-03 03:29:00        0.38   \n",
       "\n",
       "            trop_risetime  storetime_diff  trop_pct  \n",
       "0     2141-05-22 23:05:00        8.783333  5.272727  \n",
       "1     2141-05-22 23:05:00        8.783333  5.272727  \n",
       "2     2141-05-22 23:05:00        8.783333  5.272727  \n",
       "3     2141-05-22 23:05:00        8.783333  5.272727  \n",
       "4     2141-05-22 23:05:00        8.783333  5.272727  \n",
       "...                   ...             ...       ...  \n",
       "89036 2128-03-03 10:09:00        3.000000 -0.876623  \n",
       "89037 2128-03-03 10:09:00        3.000000 -0.876623  \n",
       "89038 2128-03-03 10:09:00        3.000000 -0.876623  \n",
       "89039 2128-03-03 10:09:00        3.000000 -0.876623  \n",
       "89040 2128-03-03 10:09:00        3.000000 -0.876623  \n",
       "\n",
       "[89041 rows x 8 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo = tmp_hemo.merge(trop_inc_starttime,on='subject_id',how='inner')\n",
    "tmp_hemo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c7e2404f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89041, 8)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0ec82ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to hours\n",
    "tmp_hemo['hemo_difftime'] = (tmp_hemo['trop_risetime'] - tmp_hemo['hemo_storetime']).dt.seconds.div(3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6511a504",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>hemo_difftime</th>\n",
       "      <th>hemo_valuenum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10002495</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10003502</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10007058</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10008077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10009686</td>\n",
       "      <td>23.866667</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10010058</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10010471</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10013310</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10013643</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10022584</td>\n",
       "      <td>18.683333</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id  hemo_difftime  hemo_valuenum\n",
       "0    10002495       0.716667           11.1\n",
       "1    10003502       0.566667           11.3\n",
       "2    10007058       0.350000           12.0\n",
       "3    10008077       1.000000           10.3\n",
       "4    10009686      23.866667           11.6\n",
       "5    10010058       0.750000           10.0\n",
       "6    10010471       0.350000            7.2\n",
       "7    10013310       0.566667            8.4\n",
       "8    10013643       0.650000            8.4\n",
       "9    10022584      18.683333            9.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo = tmp_hemo.groupby('subject_id')[['hemo_difftime','valuenum_x']].min().reset_index()\n",
    "tmp_hemo.rename(columns={'valuenum_x': 'hemo_valuenum'},inplace=True) \n",
    "tmp_hemo.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cc9adfe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5105, 3)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_hemo.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df611b7",
   "metadata": {},
   "source": [
    "### mortality_deathtime from trop_risetime (within 30days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "abcc500f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>deathtime_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16402</th>\n",
       "      <td>19997293</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16403</th>\n",
       "      <td>19997367</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16404</th>\n",
       "      <td>19997448</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>19997473</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16406</th>\n",
       "      <td>19997752</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16407 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id deathtime_dt\n",
       "0        10000980          NaT\n",
       "1        10002013          NaT\n",
       "2        10002430          NaT\n",
       "3        10002495          NaT\n",
       "4        10002527          NaT\n",
       "...           ...          ...\n",
       "16402    19997293          NaT\n",
       "16403    19997367          NaT\n",
       "16404    19997448          NaT\n",
       "16405    19997473          NaT\n",
       "16406    19997752          NaT\n",
       "\n",
       "[16407 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_death = df[['subject_id','deathtime_dt']].groupby('subject_id').max().reset_index()\n",
    "tmp_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "12439471",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>80493</th>\n",
       "      <td>19913577</td>\n",
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       "       subject_id        deathtime_dt\n",
       "79867    19913577 2114-06-14 20:25:00\n",
       "79868    19913577 2114-06-14 20:25:00\n",
       "79869    19913577 2114-06-14 20:25:00\n",
       "79870    19913577 2114-06-14 20:25:00\n",
       "79871    19913577 2114-06-14 20:25:00\n",
       "...           ...                 ...\n",
       "80492    19913577 2114-06-14 20:25:00\n",
       "80493    19913577 2114-06-14 20:25:00\n",
       "80494    19913577 2114-06-14 20:25:00\n",
       "80495    19913577 2114-06-14 20:25:00\n",
       "80496    19913577 2114-06-14 20:25:00\n",
       "\n",
       "[630 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
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   ],
   "source": [
    "df[df['subject_id']==19913577][['subject_id','deathtime_dt']]"
   ]
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  {
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   "execution_count": 78,
   "id": "0ccb60d5",
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   ]
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  {
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       "      subject_id deathtime_dt  valuenum       trop_risetime  storetime_diff  \\\n",
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    "tmp_death "
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  },
  {
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       "      subject_id deathtime_dt  valuenum       trop_risetime  storetime_diff  \\\n",
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       "5145    19997473          NaT      3.12 2173-09-14 00:06:00       20.966667   \n",
       "5146    19997752          NaT      0.38 2128-03-03 10:09:00        3.000000   \n",
       "\n",
       "       trop_pct  death_diff  \n",
       "0           NaN         NaN  \n",
       "1      5.272727         NaN  \n",
       "2     -0.980712         NaN  \n",
       "3      4.384615         NaN  \n",
       "4     -0.924188         NaN  \n",
       "...         ...         ...  \n",
       "5142  -0.500000         NaN  \n",
       "5143  48.000000         NaN  \n",
       "5144  -0.836735         NaN  \n",
       "5145  43.571429         NaN  \n",
       "5146  -0.876623         NaN  \n",
       "\n",
       "[5147 rows x 7 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_death['death_diff'] = (tmp_death['deathtime_dt'] - tmp_death['trop_risetime']).dt.seconds.div(3600)\n",
    "tmp_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7fee5586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "645"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_death['death_diff'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "02a63e10",
   "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>deathtime_dt</th>\n",
       "      <th>valuenum</th>\n",
       "      <th>trop_risetime</th>\n",
       "      <th>storetime_diff</th>\n",
       "      <th>trop_pct</th>\n",
       "      <th>death_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10010058</td>\n",
       "      <td>2147-11-19 04:00:00</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2147-11-18 06:57:00</td>\n",
       "      <td>14.633333</td>\n",
       "      <td>-0.007042</td>\n",
       "      <td>21.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10010471</td>\n",
       "      <td>2155-07-12 15:30:00</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2155-03-12 01:07:00</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>0.387435</td>\n",
       "      <td>14.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10024982</td>\n",
       "      <td>2203-11-10 19:10:00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2203-06-10 02:09:00</td>\n",
       "      <td>20.316667</td>\n",
       "      <td>-0.865979</td>\n",
       "      <td>17.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10046679</td>\n",
       "      <td>2172-01-24 20:50:00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2172-01-23 10:06:00</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>10.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10055344</td>\n",
       "      <td>2171-02-11 17:05:00</td>\n",
       "      <td>16.78</td>\n",
       "      <td>2171-01-11 05:51:00</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.410569</td>\n",
       "      <td>11.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5081</th>\n",
       "      <td>19883399</td>\n",
       "      <td>2133-08-20 21:30:00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2133-03-08 14:26:00</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5089</th>\n",
       "      <td>19897344</td>\n",
       "      <td>2131-05-21 18:30:00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2131-05-14 20:03:00</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>-0.584270</td>\n",
       "      <td>22.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5095</th>\n",
       "      <td>19913577</td>\n",
       "      <td>2114-06-14 20:25:00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2114-06-14 19:40:00</td>\n",
       "      <td>15.366667</td>\n",
       "      <td>-0.859594</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>19926342</td>\n",
       "      <td>2166-04-29 20:40:00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>2166-05-04 04:04:00</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>-0.950000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5127</th>\n",
       "      <td>19968075</td>\n",
       "      <td>2153-04-23 20:52:00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2153-04-22 15:05:00</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>5.783333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject_id        deathtime_dt  valuenum       trop_risetime  \\\n",
       "6       10010058 2147-11-19 04:00:00      1.41 2147-11-18 06:57:00   \n",
       "7       10010471 2155-07-12 15:30:00      5.30 2155-03-12 01:07:00   \n",
       "12      10024982 2203-11-10 19:10:00      0.52 2203-06-10 02:09:00   \n",
       "17      10046679 2172-01-24 20:50:00      0.39 2172-01-23 10:06:00   \n",
       "23      10055344 2171-02-11 17:05:00     16.78 2171-01-11 05:51:00   \n",
       "...          ...                 ...       ...                 ...   \n",
       "5081    19883399 2133-08-20 21:30:00      0.45 2133-03-08 14:26:00   \n",
       "5089    19897344 2131-05-21 18:30:00      1.11 2131-05-14 20:03:00   \n",
       "5095    19913577 2114-06-14 20:25:00      0.90 2114-06-14 19:40:00   \n",
       "5100    19926342 2166-04-29 20:40:00      0.81 2166-05-04 04:04:00   \n",
       "5127    19968075 2153-04-23 20:52:00      0.19 2153-04-22 15:05:00   \n",
       "\n",
       "      storetime_diff   trop_pct  death_diff  \n",
       "6          14.633333  -0.007042   21.050000  \n",
       "7           7.416667   0.387435   14.383333  \n",
       "12         20.316667  -0.865979   17.016667  \n",
       "17          4.350000   1.166667   10.733333  \n",
       "23          0.700000   2.410569   11.233333  \n",
       "...              ...        ...         ...  \n",
       "5081        6.700000  10.250000    7.066667  \n",
       "5089        4.300000  -0.584270   22.450000  \n",
       "5095       15.366667  -0.859594    0.750000  \n",
       "5100       23.600000  -0.950000   16.600000  \n",
       "5127        4.950000   8.500000    5.783333  \n",
       "\n",
       "[802 rows x 7 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_death_final = tmp_death[tmp_death['death_diff']<30]\n",
    "tmp_death_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b4bb0e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tmp_death['death_bin'] = tmp_death['death_diff'].apply(lambda x: 0 if  x is np.nan else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e8990187",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tmp_death['death_bin'].value_counts('normalise=True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36dadd8",
   "metadata": {},
   "source": [
    "### final df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "032b4afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245338 entries, 0 to 245337\n",
      "Data columns (total 28 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   subject_id            245338 non-null  int64         \n",
      " 1   hospital_expire_flag  245338 non-null  int64         \n",
      " 2   deathtime             35338 non-null   object        \n",
      " 3   gender                245338 non-null  object        \n",
      " 4   anchor_age            245338 non-null  int64         \n",
      " 5   dod                   91195 non-null   object        \n",
      " 6   valuenum              245338 non-null  float64       \n",
      " 7   valueuom              245338 non-null  object        \n",
      " 8   itemid                245338 non-null  int64         \n",
      " 9   charttime             245338 non-null  object        \n",
      " 10  storetime             245338 non-null  object        \n",
      " 11  icu_intime            245338 non-null  object        \n",
      " 12  icu_outtime           245338 non-null  object        \n",
      " 13  aspirin               245338 non-null  int64         \n",
      " 14  p2y12                 245338 non-null  int64         \n",
      " 15  other_antiplatelet    245338 non-null  int64         \n",
      " 16  starttime             245338 non-null  object        \n",
      " 17  stoptime              245296 non-null  object        \n",
      " 18  addmittime_dt         245338 non-null  datetime64[ns]\n",
      " 19  deathtime_dt          35338 non-null   datetime64[ns]\n",
      " 20  charttime_dt          245338 non-null  datetime64[ns]\n",
      " 21  storetime_dt          245338 non-null  datetime64[ns]\n",
      " 22  icu_intime_dt         245338 non-null  datetime64[ns]\n",
      " 23  icu_outtime_dt        245338 non-null  datetime64[ns]\n",
      " 24  starttime_dt          245338 non-null  datetime64[ns]\n",
      " 25  stoptime_dt           245296 non-null  datetime64[ns]\n",
      " 26  mortality_dod         245338 non-null  int64         \n",
      " 27  mortality_deathtime   245338 non-null  int64         \n",
      "dtypes: datetime64[ns](8), float64(1), int64(9), object(10)\n",
      "memory usage: 54.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2a641943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245338, 28)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "be3412dd",
   "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>gender</th>\n",
       "      <th>anchor_age</th>\n",
       "      <th>mortality_deathtime</th>\n",
       "      <th>icu_intime_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>F</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2189-06-27 08:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>2160-05-18 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
       "      <td>M</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>2129-06-13 00:43:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>M</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>2141-05-22 20:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2136-03-24 10:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16402</th>\n",
       "      <td>19997293</td>\n",
       "      <td>M</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>2123-12-10 21:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16403</th>\n",
       "      <td>19997367</td>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>2126-04-21 10:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16404</th>\n",
       "      <td>19997448</td>\n",
       "      <td>F</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>2122-07-23 10:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>19997473</td>\n",
       "      <td>F</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>2173-09-13 20:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16406</th>\n",
       "      <td>19997752</td>\n",
       "      <td>F</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>2128-02-03 11:09:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16407 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_id gender  anchor_age  mortality_deathtime       icu_intime_dt\n",
       "0        10000980      F          73                    0 2189-06-27 08:42:00\n",
       "1        10002013      F          53                    0 2160-05-18 10:00:00\n",
       "2        10002430      M          86                    0 2129-06-13 00:43:00\n",
       "3        10002495      M          81                    0 2141-05-22 20:18:00\n",
       "4        10002527      M          50                    0 2136-03-24 10:24:00\n",
       "...           ...    ...         ...                  ...                 ...\n",
       "16402    19997293      M          76                    0 2123-12-10 21:55:00\n",
       "16403    19997367      F          63                    0 2126-04-21 10:09:00\n",
       "16404    19997448      F          52                    0 2122-07-23 10:35:00\n",
       "16405    19997473      F          82                    0 2173-09-13 20:10:00\n",
       "16406    19997752      F          66                    0 2128-02-03 11:09:00\n",
       "\n",
       "[16407 rows x 5 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('subject_id').first().reset_index()[['subject_id','gender','anchor_age','mortality_deathtime','icu_intime_dt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5846e2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.groupby('subject_id').first().reset_index()[['subject_id','gender','mortality_deathtime','anchor_age','icu_intime_dt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b7cd01c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = starttimes_df[['subject_id','aspirin_timediff','p2y12_timediff','antiplatelet_timediff']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c7cd2bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3 = hemo_charttime_df[['subject_id','hemo_storetime']]\n",
    "df3 = tmp_hemo[['subject_id','hemo_difftime','hemo_valuenum']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ec12b7a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = trop_inc_starttime[['subject_id','trop_risetime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "22650336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df5 = tmp_death_final[['subject_id','death_diff']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "cd23b8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs1 = [df1, df2, df3, df4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3cb04f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='subject_id',how='outer'), dfs1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1da85ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id                   0\n",
       "gender                       0\n",
       "mortality_deathtime          0\n",
       "anchor_age                   0\n",
       "icu_intime_dt                0\n",
       "aspirin_timediff           589\n",
       "p2y12_timediff           13114\n",
       "antiplatelet_timediff    16385\n",
       "hemo_difftime            11302\n",
       "hemo_valuenum            11302\n",
       "trop_risetime            11260\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e20f502f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16407 entries, 0 to 16406\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   subject_id             16407 non-null  int64         \n",
      " 1   gender                 16407 non-null  object        \n",
      " 2   mortality_deathtime    16407 non-null  int64         \n",
      " 3   anchor_age             16407 non-null  int64         \n",
      " 4   icu_intime_dt          16407 non-null  datetime64[ns]\n",
      " 5   aspirin_timediff       15818 non-null  float64       \n",
      " 6   p2y12_timediff         3293 non-null   float64       \n",
      " 7   antiplatelet_timediff  22 non-null     float64       \n",
      " 8   hemo_difftime          5105 non-null   float64       \n",
      " 9   hemo_valuenum          5105 non-null   float64       \n",
      " 10  trop_risetime          5147 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(5), int64(3), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d35f6896",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_final['death_bin'] = df_final['death_diff'].apply(lambda x:0 if x is np.nan else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8654dfd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_final.drop(columns=['death_diff'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b2eea563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.929055\n",
       "1    0.070945\n",
       "Name: mortality_deathtime, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['mortality_deathtime'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "994af6ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16407, 11)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4beefc06",
   "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>gender</th>\n",
       "      <th>mortality_deathtime</th>\n",
       "      <th>anchor_age</th>\n",
       "      <th>icu_intime_dt</th>\n",
       "      <th>aspirin_timediff</th>\n",
       "      <th>p2y12_timediff</th>\n",
       "      <th>antiplatelet_timediff</th>\n",
       "      <th>hemo_difftime</th>\n",
       "      <th>hemo_valuenum</th>\n",
       "      <th>trop_risetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000980</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>2189-06-27 08:42:00</td>\n",
       "      <td>3.366667</td>\n",
       "      <td>3.366667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2189-06-27 14:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002013</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>2160-05-18 10:00:00</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002430</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>2129-06-13 00:43:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10002495</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>2141-05-22 20:18:00</td>\n",
       "      <td>11.716667</td>\n",
       "      <td>9.716667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2141-05-22 23:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10002527</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>2136-03-24 10:24:00</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id gender  mortality_deathtime  anchor_age       icu_intime_dt  \\\n",
       "0    10000980      F                    0          73 2189-06-27 08:42:00   \n",
       "1    10002013      F                    0          53 2160-05-18 10:00:00   \n",
       "2    10002430      M                    0          86 2129-06-13 00:43:00   \n",
       "3    10002495      M                    0          81 2141-05-22 20:18:00   \n",
       "4    10002527      M                    0          50 2136-03-24 10:24:00   \n",
       "\n",
       "   aspirin_timediff  p2y12_timediff  antiplatelet_timediff  hemo_difftime  \\\n",
       "0          3.366667        3.366667                    NaN            NaN   \n",
       "1          2.250000        2.250000                    NaN            NaN   \n",
       "2          4.000000             NaN                    NaN            NaN   \n",
       "3         11.716667        9.716667                    NaN       0.716667   \n",
       "4         21.500000             NaN                    NaN            NaN   \n",
       "\n",
       "   hemo_valuenum       trop_risetime  \n",
       "0            NaN 2189-06-27 14:18:00  \n",
       "1            NaN                 NaT  \n",
       "2            NaN                 NaT  \n",
       "3           11.1 2141-05-22 23:05:00  \n",
       "4            NaN                 NaT  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "183dfdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_med = df_final[['subject_id','aspirin_starttime','p2y12_starttime','p2y12_starttime','antiplatelet_starttime']]\n",
    "# df_med.head()\n",
    "# df_med.to_csv('./data/df_med.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde5788f",
   "metadata": {},
   "source": [
    "#### export final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bec8bc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('./data/final_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d93f1d0b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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