01_data_exploration_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Nicola De Cristofaro (Matr. 0522500876) | Cloud Computing Curriculum |\n",
    "| --- | --- |\n",
    "\n",
    "# Data preparation: exploration and normalization\n",
    "\n",
    "- The start point was to observe and analyze the tables in the dataset to understand how the data is distributed (Pandas libraries for Python is used to handle the tables as dataframes).\n",
    "- The common functions of Pandas DataFrame to use for an overview of the table are the following:\n",
    "\n",
    "  ``` python\n",
    "  dataframe.info()\n",
    "  dataframe.head()\n",
    "  dataframe.describe()\n",
    "  ```\n",
    "\n",
    "- After reviewing the contents of the various tables in the MIMIC database, only some tables were selected and loaded into DataFrames using Pandas because it was assumed that it wasn't necessary or useful for prediction to include all tables. \n",
    "\n",
    "So the table selected that compose the baseline dataset are the following:\n",
    "- **ADMISSIONS.csv** that defines a patient’s hospital admission,   \n",
    "- **PATIENTS.csv** that defines a single patient, \n",
    "- **DIAGNOSES_ICD.csv** that contains ICD diagnoses for patients, most notably ICD-9 diagnoses (after this matter will be better explained),\n",
    "\n",
    "Other tables will be considered, if necessary for the specific task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADMISSIONS table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>hadm_id</th>\n",
       "      <th>admittime</th>\n",
       "      <th>dischtime</th>\n",
       "      <th>deathtime</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>insurance</th>\n",
       "      <th>ethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14679932</td>\n",
       "      <td>21038362</td>\n",
       "      <td>2139-09-26 14:16:00</td>\n",
       "      <td>2139-09-28 11:30:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>UNKNOWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15585972</td>\n",
       "      <td>24941086</td>\n",
       "      <td>2123-10-07 23:56:00</td>\n",
       "      <td>2123-10-12 11:22:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11989120</td>\n",
       "      <td>21965160</td>\n",
       "      <td>2147-01-14 09:00:00</td>\n",
       "      <td>2147-01-17 14:25:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>UNKNOWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17817079</td>\n",
       "      <td>24709883</td>\n",
       "      <td>2165-12-27 17:33:00</td>\n",
       "      <td>2165-12-31 21:18:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15078341</td>\n",
       "      <td>23272159</td>\n",
       "      <td>2122-08-28 08:48:00</td>\n",
       "      <td>2122-08-30 12:32:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>BLACK/AFRICAN AMERICAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id           admittime           dischtime deathtime  \\\n",
       "0    14679932  21038362 2139-09-26 14:16:00 2139-09-28 11:30:00       NaT   \n",
       "1    15585972  24941086 2123-10-07 23:56:00 2123-10-12 11:22:00       NaT   \n",
       "2    11989120  21965160 2147-01-14 09:00:00 2147-01-17 14:25:00       NaT   \n",
       "3    17817079  24709883 2165-12-27 17:33:00 2165-12-31 21:18:00       NaT   \n",
       "4    15078341  23272159 2122-08-28 08:48:00 2122-08-30 12:32:00       NaT   \n",
       "\n",
       "  admission_type insurance               ethnicity  \n",
       "0       ELECTIVE     Other                 UNKNOWN  \n",
       "1       ELECTIVE     Other                   WHITE  \n",
       "2       ELECTIVE     Other                 UNKNOWN  \n",
       "3       ELECTIVE     Other                   OTHER  \n",
       "4       ELECTIVE     Other  BLACK/AFRICAN AMERICAN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mimic4_path = '../../mimic-iv-1.0/'\n",
    "\n",
    "# read admissions table\n",
    "def read_admissions_table(mimic4_path):\n",
    "    admits = pd.read_csv(mimic4_path + 'core/admissions.csv')\n",
    "    # Pre-emptively don't include some columns that I don't need\n",
    "    admits = admits[['subject_id','hadm_id', 'admittime', 'dischtime', 'deathtime', 'admission_type', 'insurance', 'ethnicity']]\n",
    "    # Converts dates to a proper format\n",
    "    admits.admittime = pd.to_datetime(admits.admittime)\n",
    "    admits.dischtime = pd.to_datetime(admits.dischtime)\n",
    "    admits.deathtime = pd.to_datetime(admits.deathtime)\n",
    "    return admits\n",
    "\n",
    "admits = read_admissions_table(mimic4_path)\n",
    "admits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 523740 entries, 0 to 523739\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   subject_id      523740 non-null  int64         \n",
      " 1   hadm_id         523740 non-null  int64         \n",
      " 2   admittime       523740 non-null  datetime64[ns]\n",
      " 3   dischtime       523740 non-null  datetime64[ns]\n",
      " 4   deathtime       9337 non-null    datetime64[ns]\n",
      " 5   admission_type  523740 non-null  object        \n",
      " 6   insurance       523740 non-null  object        \n",
      " 7   ethnicity       523740 non-null  object        \n",
      "dtypes: datetime64[ns](3), int64(2), object(3)\n",
      "memory usage: 32.0+ MB\n"
     ]
    }
   ],
   "source": [
    "admits.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When DEATHTIME in ADMISSIONS is not null then the patient associated died at the hospital, so we mark this distinction with a boolean variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "admits['died_at_the_hospital'] = admits['deathtime'].notnull().map({True:1, False:0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reduction number of categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WHITE                            337630\n",
       "BLACK/AFRICAN AMERICAN            80293\n",
       "HISPANIC/LATINO                   29823\n",
       "OTHER                             26813\n",
       "ASIAN                             24506\n",
       "UNKNOWN                           19400\n",
       "UNABLE TO OBTAIN                   3740\n",
       "AMERICAN INDIAN/ALASKA NATIVE      1535\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ETHNICITY\n",
    "admits['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could reduce the number of categoruies just by considering the main categories or super-categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicod\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "WHITE                     337630\n",
       "BLACK/AFRICAN AMERICAN     80293\n",
       "OTHER/UNKNOWN              51488\n",
       "HISPANIC/LATINO            29823\n",
       "ASIAN                      24506\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compress the number of ethnicity categories\n",
    "admits['ethnicity'].replace(regex=r'^ASIAN\\D*', value='ASIAN', inplace=True)\n",
    "admits['ethnicity'].replace(regex=r'^WHITE\\D*', value='WHITE', inplace=True)\n",
    "admits['ethnicity'].replace(regex=r'^HISPANIC\\D*', value='HISPANIC/LATINO', inplace=True)\n",
    "admits['ethnicity'].replace(regex=r'^BLACK\\D*', value='BLACK/AFRICAN AMERICAN', inplace=True)\n",
    "admits['ethnicity'].replace(['UNABLE TO OBTAIN', 'OTHER', 'PATIENT DECLINED TO ANSWER', \n",
    "                         'UNKNOWN/NOT SPECIFIED'], value='OTHER/UNKNOWN', inplace=True)\n",
    "#take into consideration just the top-5 categories with biggest value_count, the others will fall into OTHER category\n",
    "admits['ethnicity'].loc[~admits['ethnicity'].isin(admits['ethnicity'].value_counts().nlargest(5).index.tolist())] = 'OTHER/UNKNOWN'  \n",
    "admits['ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do the same analysis done for ETHNICITY also for other attributes, if necessary, to reduce the number of possible categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EW EMER.                       157896\n",
       "EU OBSERVATION                 100445\n",
       "ELECTIVE                        72072\n",
       "OBSERVATION ADMIT               55497\n",
       "URGENT                          47930\n",
       "SURGICAL SAME DAY ADMISSION     41074\n",
       "DIRECT EMER.                    21581\n",
       "DIRECT OBSERVATION              19991\n",
       "AMBULATORY OBSERVATION           7254\n",
       "Name: admission_type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ADMISSION_TYPE\n",
    "admits['admission_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The category URGENT is a lot similar semantically to EMERGENCY or DIRECT EMERGENCY, so could combine these categories in EMERGENCY. We could do the same process also for all those categories that are related to OBSERVATION."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMERGENCY                      227407\n",
       "EU OBSERVATION                 100445\n",
       "ELECTIVE                        72072\n",
       "OBSERVATION ADMIT               55497\n",
       "SURGICAL SAME DAY ADMISSION     41074\n",
       "DIRECT OBSERVATION              19991\n",
       "AMBULATORY OBSERVATION           7254\n",
       "Name: admission_type, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compresse into EMERGENCY\n",
    "admits['admission_type'].replace(to_replace='EW EMER.', value='EMERGENCY', inplace=True)\n",
    "admits['admission_type'].replace(to_replace='DIRECT EMER.', value='EMERGENCY', inplace=True)\n",
    "admits['admission_type'].replace(to_replace='URGENT', value='EMERGENCY', inplace=True)\n",
    "admits['admission_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMERGENCY                      227407\n",
       "OBSERVATION                    183187\n",
       "ELECTIVE                        72072\n",
       "SURGICAL SAME DAY ADMISSION     41074\n",
       "Name: admission_type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compresse into EMERGENCY\n",
    "admits['admission_type'].replace(to_replace='EU OBSERVATION', value='OBSERVATION', inplace=True)\n",
    "admits['admission_type'].replace(to_replace='OBSERVATION ADMIT', value='OBSERVATION', inplace=True)\n",
    "admits['admission_type'].replace(to_replace='DIRECT OBSERVATION', value='OBSERVATION', inplace=True)\n",
    "admits['admission_type'].replace(to_replace='AMBULATORY OBSERVATION', value='OBSERVATION', inplace=True)\n",
    "admits['admission_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PATIENTS table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>anchor_age</th>\n",
       "      <th>dod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000048</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002723</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003939</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004222</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005325</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id gender  anchor_age dod\n",
       "0    10000048      F          23 NaT\n",
       "1    10002723      F           0 NaT\n",
       "2    10003939      M           0 NaT\n",
       "3    10004222      M           0 NaT\n",
       "4    10005325      F           0 NaT"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read patients table\n",
    "def read_patients_table(mimic4_path):\n",
    "    pats = pd.read_csv(mimic4_path + 'core/patients.csv')\n",
    "    # Pre-emptively don't include some columns that I don't need\n",
    "    pats = pats[['subject_id', 'gender', 'anchor_age', 'dod']]\n",
    "    pats.dod = pd.to_datetime(pats.dod)\n",
    "    return pats\n",
    "\n",
    "patients = read_patients_table(mimic4_path)\n",
    "patients.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In PATIENTS table we have now the age of the patient and this is a news in comparison to MIMIC-3 where the age of the patient had to be computed.\n",
    "\n",
    "So, let's have a look on the age distribution between patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DOB has been shifted for patients older than 89 to obscure their age and comply with HIPAA, the median age for the patients whose date of birth was shifted is 91.4\n",
    "\n",
    "#let's see the distribution of age\n",
    "plt.hist(patients['anchor_age'], bins=20, color='#a12a52')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Age (years)')\n",
    "plt.title('Distribution of Age in MIMIC-IV')\n",
    "plt.tick_params(left=False, bottom=False, top=False, right=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from age distribution, patients in their childhood are not present, this reflects the fact that MIMIC-IV as MIMIC-III does not contain data from pediatric patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see also the distribution of gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    199707\n",
       "M    182571\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {
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   ],
   "source": [
    "# distribution of gender\n",
    "plt.hist(patients['gender'], bins=3, color='#b24b88')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Gender')\n",
    "plt.title('Distribution of Gender in MIMIC-IV')\n",
    "plt.tick_params(left=False, bottom=False, top=False, right=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wee can see how the gender is almost equally balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we merge patients and admissions tables on 'subject_id' link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   subject_id   hadm_id           admittime           dischtime deathtime  \\\n",
       "0    14679932  21038362 2139-09-26 14:16:00 2139-09-28 11:30:00       NaT   \n",
       "1    15585972  24941086 2123-10-07 23:56:00 2123-10-12 11:22:00       NaT   \n",
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       "3    17817079  24709883 2165-12-27 17:33:00 2165-12-31 21:18:00       NaT   \n",
       "4    15078341  23272159 2122-08-28 08:48:00 2122-08-30 12:32:00       NaT   \n",
       "\n",
       "  admission_type insurance               ethnicity  died_at_the_hospital  \\\n",
       "0       ELECTIVE     Other           OTHER/UNKNOWN                     0   \n",
       "1       ELECTIVE     Other                   WHITE                     0   \n",
       "2       ELECTIVE     Other           OTHER/UNKNOWN                     0   \n",
       "3       ELECTIVE     Other           OTHER/UNKNOWN                     0   \n",
       "4       ELECTIVE     Other  BLACK/AFRICAN AMERICAN                     0   \n",
       "\n",
       "  gender  anchor_age dod  \n",
       "0      F           0 NaT  \n",
       "1      F           0 NaT  \n",
       "2      M           0 NaT  \n",
       "3      F           0 NaT  \n",
       "4      M           0 NaT  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the PATIENTS table with ADMISSIONS table\n",
    "admits_patients = pd.merge(admits, patients, how='inner', on='subject_id')\n",
    "admits_patients.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DIAGNOSES_ICD table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>3</td>\n",
       "      <td>2825</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>2</td>\n",
       "      <td>V0251</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>5</td>\n",
       "      <td>V270</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>1</td>\n",
       "      <td>64891</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>4</td>\n",
       "      <td>66481</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id  seq_num icd_code  icd_version\n",
       "0    15734973  20475282        3     2825            9\n",
       "1    15734973  20475282        2    V0251            9\n",
       "2    15734973  20475282        5     V270            9\n",
       "3    15734973  20475282        1    64891            9\n",
       "4    15734973  20475282        4    66481            9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read diagnoses_icd table\n",
    "def read_diagnoses_icd_table(mimic4_path):\n",
    "    diag_icds = pd.read_csv(mimic4_path + 'hosp/diagnoses_icd.csv')\n",
    "    return diag_icds\n",
    "\n",
    "diag_icds = read_diagnoses_icd_table(mimic4_path)\n",
    "diag_icds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_id     5280351\n",
       "hadm_id        5280351\n",
       "seq_num        5280351\n",
       "icd_code       5280351\n",
       "icd_version    5280351\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag_icds.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     3090370\n",
       "10    2189981\n",
       "Name: icd_version, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag_icds.icd_version.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice that there are two version of icd_code: version 9 and 10. In general, ICD-10 codes are more detailed, but they could be mapped and converted to ICD-9 because they expresse the same concept."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are dealing with a dataframe with a lot of entries and in any case it should later be reduced in size for a faster test, for simplicity we consider only the diagnoses with ICD9 codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    3090370\n",
       "Name: icd_version, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag_icds = diag_icds[diag_icds['icd_version'] == 9]\n",
    "diag_icds.icd_version.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "International Classification of Diseases, Clinical Modification (ICD-CM in version 9 and 10) is an adaption created by the U.S. National Center for Health Statistics (NCHS) and used in assigning diagnostic and procedure codes associated with inpatient, outpatient, and physician office utilization in the United States."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9534 unique ICD9 codes in this dataset.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} unique ICD9 codes in this dataset.'.format(diag_icds['icd_code'].value_counts().count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because it's not feasible to have all these unique values to use as features for predicting LOS, it is necessary to reduce the diagnosis into more general categories. After researching the ICD9 and ICD10 approach, it's been noticed that they are arranged into super categories as described at the following links:\n",
    "\n",
    "- **ICD-9 codes supercategories**: https://en.wikipedia.org/wiki/List_of_ICD-9_codes\n",
    "- **ICD-10 codes supercategories**: https://en.wikipedia.org/wiki/ICD-10#Chapters\n",
    "\n",
    "From this research we see that our attention could be just on the first 3 values to discover the supercategory. So our task now is to recode each ICD code considedred to its supercategory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>hadm_id</th>\n",
       "      <th>seq_num</th>\n",
       "      <th>icd_code</th>\n",
       "      <th>icd_version</th>\n",
       "      <th>recode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>3</td>\n",
       "      <td>2825</td>\n",
       "      <td>9</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>2</td>\n",
       "      <td>V0251</td>\n",
       "      <td>9</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>5</td>\n",
       "      <td>V270</td>\n",
       "      <td>9</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>1</td>\n",
       "      <td>64891</td>\n",
       "      <td>9</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>4</td>\n",
       "      <td>66481</td>\n",
       "      <td>9</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id  seq_num icd_code  icd_version  recode\n",
       "0    15734973  20475282        3     2825            9     282\n",
       "1    15734973  20475282        2    V0251            9     999\n",
       "2    15734973  20475282        5     V270            9     999\n",
       "3    15734973  20475282        1    64891            9     648\n",
       "4    15734973  20475282        4    66481            9     664"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out E and V codes from ICD9 codes since processing will be done on the numeric first 3 values\n",
    "diag_icds['recode'] = diag_icds['icd_code']\n",
    "diag_icds['recode'] = diag_icds['recode'][~diag_icds['recode'].str.contains(\"[a-zA-Z]\").fillna(False)]\n",
    "diag_icds['recode'].fillna(value='999', inplace=True)\n",
    "\n",
    "# Take in consideration just the first 3 integers of the ICD9 code\n",
    "diag_icds['recode'] = diag_icds['recode'].str.slice(start=0, stop=3, step=1)\n",
    "diag_icds['recode'] = diag_icds['recode'].astype(int)\n",
    "diag_icds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>hadm_id</th>\n",
       "      <th>seq_num</th>\n",
       "      <th>icd_code</th>\n",
       "      <th>icd_version</th>\n",
       "      <th>recode</th>\n",
       "      <th>super_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>3</td>\n",
       "      <td>2825</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>2</td>\n",
       "      <td>V0251</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>injury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>5</td>\n",
       "      <td>V270</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>injury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>1</td>\n",
       "      <td>64891</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15734973</td>\n",
       "      <td>20475282</td>\n",
       "      <td>4</td>\n",
       "      <td>66481</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>pregnancy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id  seq_num icd_code  icd_version  recode super_category\n",
       "0    15734973  20475282        3     2825            9       3          blood\n",
       "1    15734973  20475282        2    V0251            9      16         injury\n",
       "2    15734973  20475282        5     V270            9      16         injury\n",
       "3    15734973  20475282        1    64891            9      10      pregnancy\n",
       "4    15734973  20475282        4    66481            9      10      pregnancy"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ICD-9 Main Category ranges\n",
    "icd9_ranges = [(1, 140), (140, 240), (240, 280), (280, 290), (290, 320), (320, 390), \n",
    "               (390, 460), (460, 520), (520, 580), (580, 630), (630, 680), (680, 710),\n",
    "               (710, 740), (740, 760), (760, 780), (780, 800), (800, 1000), (1000, 2000)]\n",
    "\n",
    "# Associated category names\n",
    "diag_dict = {0: 'infectious', 1: 'neoplasms', 2: 'endocrine', 3: 'blood',\n",
    "             4: 'mental', 5: 'nervous', 6: 'circulatory', 7: 'respiratory',\n",
    "             8: 'digestive', 9: 'genitourinary', 10: 'pregnancy', 11: 'skin', \n",
    "             12: 'muscular', 13: 'congenital', 14: 'prenatal', 15: 'misc',\n",
    "             16: 'injury', 17: 'misc'}\n",
    "\n",
    "# Re-code in terms of integer\n",
    "for num, cat_range in enumerate(icd9_ranges):\n",
    "    diag_icds['recode'] = np.where(diag_icds['recode'].between(cat_range[0],cat_range[1]), num, diag_icds['recode'])\n",
    "    \n",
    "# Convert integer to category name using diag_dict\n",
    "diag_icds['super_category'] = diag_icds['recode'].replace(diag_dict)\n",
    "diag_icds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each admission, usually there is more than one diagnosis. Often, there are more than 1 diagnoses for 1 category. \n",
    "\n",
    "We could create a matrix that highlights all the diagnoses for each admission. This should not be done on the SUBJECT_ID since each patient could have different diagnoses for each admission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>hadm_id</th>\n",
       "      <th>super_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000019</td>\n",
       "      <td>[injury, blood, respiratory, circulatory, endo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20000041</td>\n",
       "      <td>[endocrine, digestive, endocrine, injury, musc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000055</td>\n",
       "      <td>[injury, injury]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000057</td>\n",
       "      <td>[respiratory, injury, nervous, injury, injury,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20000095</td>\n",
       "      <td>[injury, injury, injury]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hadm_id                                     super_category\n",
       "0  20000019  [injury, blood, respiratory, circulatory, endo...\n",
       "1  20000041  [endocrine, digestive, endocrine, injury, musc...\n",
       "2  20000055                                   [injury, injury]\n",
       "3  20000057  [respiratory, injury, nervous, injury, injury,...\n",
       "4  20000095                           [injury, injury, injury]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of diagnoses for each admission\n",
    "hadm_list = diag_icds.groupby('hadm_id')['super_category'].apply(list).reset_index()\n",
    "hadm_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blood</th>\n",
       "      <th>circulatory</th>\n",
       "      <th>congenital</th>\n",
       "      <th>digestive</th>\n",
       "      <th>endocrine</th>\n",
       "      <th>genitourinary</th>\n",
       "      <th>infectious</th>\n",
       "      <th>injury</th>\n",
       "      <th>mental</th>\n",
       "      <th>misc</th>\n",
       "      <th>muscular</th>\n",
       "      <th>neoplasms</th>\n",
       "      <th>nervous</th>\n",
       "      <th>pregnancy</th>\n",
       "      <th>prenatal</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   blood  circulatory  congenital  digestive  endocrine  genitourinary  \\\n",
       "0      1            1           1          0          3              2   \n",
       "1      0            1           0          1          3              0   \n",
       "2      0            0           0          0          0              0   \n",
       "3      1            1           0          0          1              0   \n",
       "4      0            0           0          0          0              0   \n",
       "\n",
       "   infectious  injury  mental  misc  muscular  neoplasms  nervous  pregnancy  \\\n",
       "0           1       2       0     0         0          0        0          0   \n",
       "1           0       4       0     0         1          0        0          0   \n",
       "2           0       2       0     0         0          0        0          0   \n",
       "3           0       7       0     1         5          0        4          0   \n",
       "4           0       3       0     0         0          0        0          0   \n",
       "\n",
       "   prenatal  respiratory  skin  \n",
       "0         0            1     0  \n",
       "1         0            0     0  \n",
       "2         0            0     0  \n",
       "3         1            2     0  \n",
       "4         0            0     0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert diagnoses list into hospital admission-item matrix\n",
    "hadm_item = pd.get_dummies(hadm_list['super_category'].apply(pd.Series).stack()).sum(level=0)\n",
    "hadm_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join back with HADM_ID\n",
    "hadm_item = hadm_item.join(hadm_list['hadm_id'], how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>hadm_id</th>\n",
       "      <th>admittime</th>\n",
       "      <th>dischtime</th>\n",
       "      <th>deathtime</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>insurance</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>died_at_the_hospital</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>injury</th>\n",
       "      <th>mental</th>\n",
       "      <th>misc</th>\n",
       "      <th>muscular</th>\n",
       "      <th>neoplasms</th>\n",
       "      <th>nervous</th>\n",
       "      <th>pregnancy</th>\n",
       "      <th>prenatal</th>\n",
       "      <th>respiratory</th>\n",
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       "      <td>14679932</td>\n",
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       "      <td>15585972</td>\n",
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       "      <td>2123-10-07 23:56:00</td>\n",
       "      <td>2123-10-12 11:22:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2122-08-28 08:48:00</td>\n",
       "      <td>2122-08-30 12:32:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>BLACK/AFRICAN AMERICAN</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17301855</td>\n",
       "      <td>29732723</td>\n",
       "      <td>2140-06-06 14:23:00</td>\n",
       "      <td>2140-06-08 14:25:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>Other</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2181-07-10 20:28:00</td>\n",
       "      <td>2181-07-12 15:49:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>ELECTIVE</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id           admittime           dischtime deathtime  \\\n",
       "0    14679932  21038362 2139-09-26 14:16:00 2139-09-28 11:30:00       NaT   \n",
       "1    15585972  24941086 2123-10-07 23:56:00 2123-10-12 11:22:00       NaT   \n",
       "2    15078341  23272159 2122-08-28 08:48:00 2122-08-30 12:32:00       NaT   \n",
       "3    17301855  29732723 2140-06-06 14:23:00 2140-06-08 14:25:00       NaT   \n",
       "4    17991012  24298836 2181-07-10 20:28:00 2181-07-12 15:49:00       NaT   \n",
       "\n",
       "  admission_type insurance               ethnicity  died_at_the_hospital  \\\n",
       "0       ELECTIVE     Other           OTHER/UNKNOWN                     0   \n",
       "1       ELECTIVE     Other                   WHITE                     0   \n",
       "2       ELECTIVE     Other  BLACK/AFRICAN AMERICAN                     0   \n",
       "3       ELECTIVE     Other                   WHITE                     0   \n",
       "4       ELECTIVE     Other                   WHITE                     0   \n",
       "\n",
       "  gender  ...  injury mental  misc  muscular  neoplasms  nervous  pregnancy  \\\n",
       "0      F  ...       2      0     0         0          0        0          0   \n",
       "1      F  ...       2      0     0         0          0        0          0   \n",
       "2      M  ...       3      0     0         0          0        0          0   \n",
       "3      F  ...       2      0     0         0          0        0          0   \n",
       "4      M  ...       2      0     0         0          0        0          0   \n",
       "\n",
       "   prenatal  respiratory  skin  \n",
       "0         0            0     0  \n",
       "1         0            0     0  \n",
       "2         0            0     0  \n",
       "3         1            0     0  \n",
       "4         0            0     0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge with main dataframe\n",
    "admits_patients_diag = pd.merge(admits_patients, hadm_item, how='inner', on='hadm_id')\n",
    "admits_patients_diag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save this version of the dataframe to a csv. It will be used as baseline for our prediction tasks\n",
    "admits_patients_diag.to_csv('admits_patients_diag.csv')"
   ]
  }
 ],
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