Investigating EHR data from PIC-SURE - python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f15c7b43",
   "metadata": {},
   "source": [
    "# Investigating EHR data from PIC-SURE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c76af6",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to help researchers get started with EHR analysis in python using clinical data exported from PIC-SURE. \n",
    "\n",
    "### About the data\n",
    "This example code uses a synthetically generated dataset from Synthea, an open-source patient population simulation. \n",
    "The dataset has been formatted to mirror what a data export from PIC-SURE may look like.  \n",
    "It includes all age groups and is not limited to pediatric patients. \n",
    "\n",
    "\n",
    "\n",
    "Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c82c23",
   "metadata": {},
   "source": [
    "###  Installing packages\n",
    "The following code installs and loads packages needed for basic data management and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "640d6363",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/seaborn/_statistics.py:32: UserWarning: A NumPy version >=1.23.5 and <2.3.0 is required for this version of SciPy (detected version 1.22.4)\n",
      "  from scipy.stats import gaussian_kde\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d8dfa59",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baadad31",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "The following code downloads the dataset. <span style=\"color:red\">Edit the loading code below once the dataset is hosted on S3</span>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e968845e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9145/3941741903.py:2: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data = pd.read_csv('../studies/synthea_10k_picsure_format.csv')\n"
     ]
    }
   ],
   "source": [
    "#loading the dataset:\n",
    "data = pd.read_csv('../studies/synthea_10k_picsure_format.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be610ef",
   "metadata": {},
   "source": [
    "## Taking a first look at the dataset\n",
    "Let's use the `head()` function to take a first look at the dataset. \n",
    "You'll see 5 columns:\n",
    "- PATIENT_NUM: Represents a unique patient identifier\n",
    "- CONCEPT_PATH: Represents the clinical variable for a given observation (row)\n",
    "- TVAL_CHAR: The text / character value of the clinical variable, if applicable\n",
    "- NVAL_NUM: The numerical value of the clinical variable, if applicable\n",
    "- TIMESTAMP: The date and time associated to a given observation (row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22353812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>PATIENT_NUM</th>\n",
       "      <th>CONCEPT_PATH</th>\n",
       "      <th>TVAL_CHAR</th>\n",
       "      <th>NVAL_NUM</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55f11536-c06f-87bc-971e-7a39b232ecd3</td>\n",
       "      <td>\\ACT Demographics\\Sex\\</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-01T16:25:50Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55f11536-c06f-87bc-971e-7a39b232ecd3</td>\n",
       "      <td>\\ACT Demographics\\Race\\</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-01T16:25:50Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55f11536-c06f-87bc-971e-7a39b232ecd3</td>\n",
       "      <td>\\ACT Demographics\\Hispanic\\</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-01T16:25:50Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td>\n",
       "      <td>\\ACT Demographics\\Sex\\</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-01T16:25:50Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td>\n",
       "      <td>\\ACT Demographics\\Race\\</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-07-01T16:25:50Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            PATIENT_NUM                 CONCEPT_PATH  \\\n",
       "0  55f11536-c06f-87bc-971e-7a39b232ecd3       \\ACT Demographics\\Sex\\   \n",
       "1  55f11536-c06f-87bc-971e-7a39b232ecd3      \\ACT Demographics\\Race\\   \n",
       "2  55f11536-c06f-87bc-971e-7a39b232ecd3  \\ACT Demographics\\Hispanic\\   \n",
       "3  6225749a-9311-ab4e-d2a3-d9b1f919866d       \\ACT Demographics\\Sex\\   \n",
       "4  6225749a-9311-ab4e-d2a3-d9b1f919866d      \\ACT Demographics\\Race\\   \n",
       "\n",
       "  TVAL_CHAR  NVAL_NUM             TIMESTAMP  \n",
       "0    Female       NaN  2024-07-01T16:25:50Z  \n",
       "1     White       NaN  2024-07-01T16:25:50Z  \n",
       "2        No       NaN  2024-07-01T16:25:50Z  \n",
       "3      Male       NaN  2024-07-01T16:25:50Z  \n",
       "4     White       NaN  2024-07-01T16:25:50Z  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0208c8",
   "metadata": {},
   "source": [
    "The EHR data represented in this dataset may be very large, making it difficult to explore manually. \n",
    "\n",
    "You'll notice that the `CONCEPT_PATH` column consists of a hierarchy that narrows down to a certain clinical variable. \n",
    "\n",
    "Let's take a look at the highest level of the `CONCEPT_PATH` to see what kind of data we have. We can consider this to be the `VARIABLE_TYPE`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9dd50c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VARIABLE_TYPE\n",
       "ACT Lab Test Results    2788447\n",
       "ACT Medications          748721\n",
       "ACT Diagnosis ICD-10      96705\n",
       "ACT Procedures CPT        55808\n",
       "ACT Demographics          48222\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolate the variable type using regular expressions, and create a new column for variable type\n",
    "data['VARIABLE_TYPE'] = data['CONCEPT_PATH'].str.extract(r'\\\\([^\\\\]+)\\\\')\n",
    "# Count how many observations are associated with each variable type\n",
    "data['VARIABLE_TYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08aa83b7",
   "metadata": {},
   "source": [
    "As seen above, this dataset contains the following data types: \n",
    "- ACT Lab Test Results (Labs)\n",
    "- ACT Medications\n",
    "- ACT Diagnosis ICD-10 (Diagnoses)\n",
    "- ACT Procedures CPT (Procedures)\n",
    "- ACT Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a123bda5",
   "metadata": {},
   "source": [
    "This dataset should contain about 10,000 synthetic patient records. Let's verify this by checking the count of unique patient IDs present in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03729a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PATIENT_NUM'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b749adfe",
   "metadata": {},
   "source": [
    "Let's also check what date ranges are represented in our dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df600579",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Changing the time stamp to a date format\n",
    "data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n",
    "data['DATE'] = data['TIMESTAMP'].dt.date\n",
    "\n",
    "\n",
    "# Create a histogram of dates\n",
    "plt.figure(figsize=(12, 6))\n",
    "data['DATE'].hist(bins=30, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Dates')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2fd7d9",
   "metadata": {},
   "source": [
    "The majority of our dataset is from the last 15 years, as expected. \n",
    "\n",
    "We also see data stretching far back to 1920; perhaps this is associated with a patient's age. Depending on your research question, you may want to investigate those outliers. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef552dd9",
   "metadata": {},
   "source": [
    "## Exploring Demographic Data\n",
    "Here we will explore, organize and visualize the demographic data available in our dataset. This includes age, sex and race.\n",
    "\n",
    "Let's create a new dataframe called `demo_df` that will only include the demographics information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ce1c34e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "demo_df = data[data['VARIABLE_TYPE'] == 'ACT Demographics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59720149",
   "metadata": {},
   "source": [
    "Let's first take a look at the **sex** breakdown. \n",
    "\n",
    "We can find the Sex related observations by searching for this value in the CONCEPT_PATH column. Then, we take the sex related values (found in the TVAL_CHAR column) and transform them into a new column, \"sex\". \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb986f34",
   "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>sex</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>5747</td>\n",
       "      <td>49.355891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>5897</td>\n",
       "      <td>50.644109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  count  percentage\n",
       "0  Female   5747   49.355891\n",
       "1    Male   5897   50.644109"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isolating and showing sex data:\n",
    "sex_df = demo_df[demo_df['CONCEPT_PATH'].str.contains(r'\\\\Sex\\\\', na=False)]\n",
    "\n",
    "#creating a new dataset only including the patient id#, sex and changing column name to 'sex':\n",
    "sex_df = sex_df[['PATIENT_NUM', 'TVAL_CHAR']].rename(columns={'TVAL_CHAR': 'sex'})\n",
    "\n",
    "sex_df \n",
    "\n",
    "\n",
    "#grouping by sex and calculating the percentage of each:\n",
    "sex_count = (sex_df.groupby('sex')\n",
    "              .size()\n",
    "              .reset_index(name='count'))\n",
    "sex_count['percentage'] = (sex_count['count'] / sex_count['count'].sum()) * 100\n",
    "\n",
    "sex_count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d3a9a6",
   "metadata": {},
   "source": [
    "Now let's look at the **age** distribution of our patients. \n",
    "\n",
    "Below, we isolate the age data by searching the CONCEPT_PATH for Age. Then, we take the age related values (found in the NVAL_NUM column) and transform them into a new column, \"age\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "94dd1187",
   "metadata": {
    "scrolled": true
   },
   "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>PATIENT_NUM</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36578</th>\n",
       "      <td>55f11536-c06f-87bc-971e-7a39b232ecd3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36579</th>\n",
       "      <td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36580</th>\n",
       "      <td>fe16b3d3-424a-86ce-00df-cd1386d7279f</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36581</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36582</th>\n",
       "      <td>6d316c1c-7ce7-7120-7f88-b9f0531ddb6f</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48217</th>\n",
       "      <td>fe052b64-479b-d856-3d1f-e147f6178b18</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48218</th>\n",
       "      <td>a9b32450-3d19-06b2-d777-6531dfae18e6</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48219</th>\n",
       "      <td>8ce5bd67-f209-f056-03a4-7b75b46d9474</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48220</th>\n",
       "      <td>b5d68396-bda5-d8e1-f46f-2414855e7498</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48221</th>\n",
       "      <td>5c41e233-1551-fed2-89e4-9bb9c30789eb</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11644 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PATIENT_NUM   age\n",
       "36578  55f11536-c06f-87bc-971e-7a39b232ecd3   5.0\n",
       "36579  6225749a-9311-ab4e-d2a3-d9b1f919866d  15.0\n",
       "36580  fe16b3d3-424a-86ce-00df-cd1386d7279f   7.0\n",
       "36581  2840480c-a43b-f0eb-916d-66aca598a621  59.0\n",
       "36582  6d316c1c-7ce7-7120-7f88-b9f0531ddb6f   9.0\n",
       "...                                     ...   ...\n",
       "48217  fe052b64-479b-d856-3d1f-e147f6178b18  57.0\n",
       "48218  a9b32450-3d19-06b2-d777-6531dfae18e6  73.0\n",
       "48219  8ce5bd67-f209-f056-03a4-7b75b46d9474  65.0\n",
       "48220  b5d68396-bda5-d8e1-f46f-2414855e7498  77.0\n",
       "48221  5c41e233-1551-fed2-89e4-9bb9c30789eb  69.0\n",
       "\n",
       "[11644 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isolating and showing age data:\n",
    "age_df = demo_df[demo_df['CONCEPT_PATH'].str.contains(r'\\\\Age\\\\')]\n",
    "\n",
    "#creating a new dataset only including the patient id#, age and changing column name to 'age':\n",
    "age_df = age_df[['PATIENT_NUM', 'NVAL_NUM']].rename(columns={'NVAL_NUM': 'age'})\n",
    "\n",
    "age_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2f61d6",
   "metadata": {},
   "source": [
    "We can now use this age dataframe to plot the age distribution in our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21c55f00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a histogram demonstrating the age distribution:\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(age_df['age'], bins=110, color='blue', edgecolor='black')\n",
    "plt.title('Age Distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71cd65a7",
   "metadata": {},
   "source": [
    "We can also group our patients into age groups and create a table detailing how many patients are in each age group. \n",
    "\n",
    "We will group our patients into the following groups: \n",
    "- under 18\n",
    "- 18-65\n",
    "- 65+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91ffe9fb",
   "metadata": {
    "scrolled": true
   },
   "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>age_group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt;18</td>\n",
       "      <td>2213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-65</td>\n",
       "      <td>7413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65+</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_group  count\n",
       "0       <18   2213\n",
       "1     18-65   7413\n",
       "2       65+   2018"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping by age (<18 , 18-65, 65+)\n",
    "\n",
    "age_group= age_df.copy()\n",
    "\n",
    "bins = [-float('inf'), 17, 64, float('inf')]\n",
    "labels = ['<18', '18-65', '65+']\n",
    "\n",
    "age_group['age_group'] = pd.cut(age_group['age'], bins=bins, labels=labels, right=True)\n",
    "\n",
    "\n",
    "# Calculate the number of patients in each age group\n",
    "age_group_sum = age_group['age_group'].value_counts().reindex(labels).reset_index()\n",
    "age_group_sum.columns = ['age_group', 'count']\n",
    "\n",
    "age_group_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdf0e84",
   "metadata": {},
   "source": [
    "Finally, let's investigate the **race** distribution. \n",
    "\n",
    "Similarly to above, we isolate the race data by searching the CONCEPT_PATH for Race. Then, we take the race related values (found in the TVAL_CHAR column) and transform them into a new column, \"race\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24f9b5cf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#isolating and showing race data:\n",
    "race_data = demo_df[demo_df['CONCEPT_PATH'].str.contains(r'\\\\Race\\\\', na=False)]\n",
    "\n",
    "#creating a new dataset only including the patient id#, age and changing column name to 'age':\n",
    "race_data = race_data[['PATIENT_NUM', 'TVAL_CHAR']].rename(columns={'TVAL_CHAR': 'race'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "887634ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>count</th>\n",
       "      <th>percentage</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>9648</td>\n",
       "      <td>82.858124</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black or African American</th>\n",
       "      <td>918</td>\n",
       "      <td>7.883889</td>\n",
       "      <td>Black or African American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>786</td>\n",
       "      <td>6.750258</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native Hawaiian or Other Pacific Islander</th>\n",
       "      <td>118</td>\n",
       "      <td>1.013397</td>\n",
       "      <td>Native Hawaiian or Other Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Information</th>\n",
       "      <td>113</td>\n",
       "      <td>0.970457</td>\n",
       "      <td>No Information</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Indian or Alaska Native</th>\n",
       "      <td>61</td>\n",
       "      <td>0.523875</td>\n",
       "      <td>American Indian or Alaska Native</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           count  percentage  \\\n",
       "race                                                           \n",
       "White                                       9648   82.858124   \n",
       "Black or African American                    918    7.883889   \n",
       "Asian                                        786    6.750258   \n",
       "Native Hawaiian or Other Pacific Islander    118    1.013397   \n",
       "No Information                               113    0.970457   \n",
       "American Indian or Alaska Native              61    0.523875   \n",
       "\n",
       "                                                                                race  \n",
       "race                                                                                  \n",
       "White                                                                          White  \n",
       "Black or African American                                  Black or African American  \n",
       "Asian                                                                          Asian  \n",
       "Native Hawaiian or Other Pacific Islander  Native Hawaiian or Other Pacific Islander  \n",
       "No Information                                                        No Information  \n",
       "American Indian or Alaska Native                    American Indian or Alaska Native  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping by race and calculating the percentage of each:\n",
    "\n",
    "count= race_data['race'].value_counts()\n",
    "percentages = race_data['race'].value_counts(normalize=True) * 100\n",
    "\n",
    "race_count= pd.DataFrame({\n",
    "    'count': count,\n",
    "    'percentage': percentages, \n",
    "    'race' : count.index\n",
    "    })\n",
    "race_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5e93c0",
   "metadata": {},
   "source": [
    "We will plot these results in a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b256ecf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9145/1025067948.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=race_count, x='race', y='percentage', palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a bar plot representing race distribution:\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=race_count, x='race', y='percentage', palette='viridis')\n",
    "plt.title('Percentage by Race')\n",
    "plt.xlabel('Race')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119f6c8a",
   "metadata": {},
   "source": [
    "The way our initial dataset is formatted can be difficult to view multiple variables about a given patient at the same time. \n",
    "\n",
    "Below, we create one table presenting all demographic data (age, sex, race) by patient ID. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5df8862",
   "metadata": {
    "scrolled": true
   },
   "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>PATIENT_NUM</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55f11536-c06f-87bc-971e-7a39b232ecd3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6225749a-9311-ab4e-d2a3-d9b1f919866d</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fe16b3d3-424a-86ce-00df-cd1386d7279f</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6d316c1c-7ce7-7120-7f88-b9f0531ddb6f</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11639</th>\n",
       "      <td>fe052b64-479b-d856-3d1f-e147f6178b18</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11640</th>\n",
       "      <td>a9b32450-3d19-06b2-d777-6531dfae18e6</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11641</th>\n",
       "      <td>8ce5bd67-f209-f056-03a4-7b75b46d9474</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black or African American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11642</th>\n",
       "      <td>b5d68396-bda5-d8e1-f46f-2414855e7498</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11643</th>\n",
       "      <td>5c41e233-1551-fed2-89e4-9bb9c30789eb</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black or African American</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11644 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PATIENT_NUM   age     sex  \\\n",
       "0      55f11536-c06f-87bc-971e-7a39b232ecd3   5.0  Female   \n",
       "1      6225749a-9311-ab4e-d2a3-d9b1f919866d  15.0    Male   \n",
       "2      fe16b3d3-424a-86ce-00df-cd1386d7279f   7.0    Male   \n",
       "3      2840480c-a43b-f0eb-916d-66aca598a621  59.0  Female   \n",
       "4      6d316c1c-7ce7-7120-7f88-b9f0531ddb6f   9.0  Female   \n",
       "...                                     ...   ...     ...   \n",
       "11639  fe052b64-479b-d856-3d1f-e147f6178b18  57.0    Male   \n",
       "11640  a9b32450-3d19-06b2-d777-6531dfae18e6  73.0  Female   \n",
       "11641  8ce5bd67-f209-f056-03a4-7b75b46d9474  65.0  Female   \n",
       "11642  b5d68396-bda5-d8e1-f46f-2414855e7498  77.0    Male   \n",
       "11643  5c41e233-1551-fed2-89e4-9bb9c30789eb  69.0  Female   \n",
       "\n",
       "                            race  \n",
       "0                          White  \n",
       "1                          White  \n",
       "2                          White  \n",
       "3                          White  \n",
       "4                          White  \n",
       "...                          ...  \n",
       "11639                      White  \n",
       "11640                      White  \n",
       "11641  Black or African American  \n",
       "11642                      White  \n",
       "11643  Black or African American  \n",
       "\n",
       "[11644 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging all columns to show age, sex, race in one table:\n",
    "\n",
    "first_merge = pd.merge(age_df[['PATIENT_NUM', 'age']], sex_df[['PATIENT_NUM', 'sex']], on='PATIENT_NUM', how='left')\n",
    "\n",
    "demographics_joined = pd.merge(first_merge, race_data[['PATIENT_NUM', 'race']], on='PATIENT_NUM', how='left')\n",
    "\n",
    "demographics_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53546716",
   "metadata": {},
   "source": [
    "## Investigating ICD codes: Epilepsy case study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123e6fec",
   "metadata": {},
   "source": [
    "We can isolate the diagnosis related information by searching for 'ACT Diagnosis' in the CONCEPT_PATH column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38614c81",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#create a new data frame including only the patients diagnosis data:\n",
    "\n",
    "diagnosis_icd10 = data[data['CONCEPT_PATH'].str.contains(r'\\\\ACT Diagnosis ICD-10\\\\')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4734862",
   "metadata": {},
   "source": [
    "### In this case study, we will investigate epilepsy as the disease of interest. \n",
    "\n",
    "Epilapsy is represented by  ICD group code \"G40\", which includes the following and their sub-groups:\n",
    "- G40.0: Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset.\n",
    "- G40.1: Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures.\n",
    "- G40.2: Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures.\n",
    "- G40.3: Generalized idiopathic epilepsy and epileptic syndromes\n",
    "- G40.A: Absence epileptic syndrome.\n",
    "- G40.B: Juvenile myoclonic epilepsy [impulsive petit mal].\n",
    "- G40.C: Lafora progressive myoclonus epilepsy, intractable.\n",
    "- G40.4: Other generalized epilepsy and epileptic syndromes.\n",
    "- G40.5: Epileptic seizures related to external causes.\n",
    "- G40.8: Other epilepsy and recurrent seizures.\n",
    "- G40.9: Epilepsy, unspecified.\n",
    "\n",
    "*A full list of ICD-10 codes can be found at https://www.icd10data.com/ICD10CM/Codes*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfa41de",
   "metadata": {},
   "source": [
    "### Isolating the patient cohort\n",
    "\n",
    "First, we create a new data-frame that only includes patients with epilepsy.\n",
    "\n",
    "We create a variable called `icd_code_of_interest` and set it to `G40`.\n",
    "\n",
    "(you can replace the ICD code \"G40\" with the ICD code of your choice if you are interested in investigating a different disease group!):\n",
    "\n",
    "You can see a preview of this epilepsy focused dataset below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d49bab50",
   "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",
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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>PATIENT_NUM</th>\n",
       "      <th>CONCEPT_PATH</th>\n",
       "      <th>icd_10_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48255</th>\n",
       "      <td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td>\n",
       "      <td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td>\n",
       "      <td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48256</th>\n",
       "      <td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td>\n",
       "      <td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td>\n",
       "      <td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48257</th>\n",
       "      <td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td>\n",
       "      <td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td>\n",
       "      <td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48258</th>\n",
       "      <td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td>\n",
       "      <td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td>\n",
       "      <td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48259</th>\n",
       "      <td>e6ca8432-abc9-e3d4-4e5d-47f972f07181</td>\n",
       "      <td>\\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable</td>\n",
       "      <td>G40.909 Epilepsy, unspecified, not intractable, without status epilepticus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PATIENT_NUM  \\\n",
       "48255  e6ca8432-abc9-e3d4-4e5d-47f972f07181   \n",
       "48256  e6ca8432-abc9-e3d4-4e5d-47f972f07181   \n",
       "48257  e6ca8432-abc9-e3d4-4e5d-47f972f07181   \n",
       "48258  e6ca8432-abc9-e3d4-4e5d-47f972f07181   \n",
       "48259  e6ca8432-abc9-e3d4-4e5d-47f972f07181   \n",
       "\n",
       "                                                                                                                                                                                                                                   CONCEPT_PATH  \\\n",
       "48255  \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable   \n",
       "48256  \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable   \n",
       "48257  \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable   \n",
       "48258  \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable   \n",
       "48259  \\ACT Diagnosis ICD-10\\G00-G99 Diseases of the nervous system (G00-G99)\\G40-G47 Episodic and paroxysmal disorders (G40-G47)\\G40 Epilepsy and recurrent seizures\\G40.9 Epilepsy, unspecified\\G40.90 Epilepsy, unspecified, not intractable   \n",
       "\n",
       "                                                                      icd_10_code  \n",
       "48255  G40.909 Epilepsy, unspecified, not intractable, without status epilepticus  \n",
       "48256  G40.909 Epilepsy, unspecified, not intractable, without status epilepticus  \n",
       "48257  G40.909 Epilepsy, unspecified, not intractable, without status epilepticus  \n",
       "48258  G40.909 Epilepsy, unspecified, not intractable, without status epilepticus  \n",
       "48259  G40.909 Epilepsy, unspecified, not intractable, without status epilepticus  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icd_code_of_interest = 'G40'\n",
    "\n",
    "#filtering to include only patients diagnosed with epilepsy and recurrent seizures (ICD GROUP CODE G40):\n",
    "\n",
    "epilepsy_icd10 = diagnosis_icd10[diagnosis_icd10['TVAL_CHAR'].str.contains(icd_code_of_interest)]\n",
    "epilepsy_icd10 = epilepsy_icd10[['PATIENT_NUM', 'CONCEPT_PATH', 'TVAL_CHAR']].rename(columns={'TVAL_CHAR': 'icd_10_code'})\n",
    "\n",
    "# creating a final list of patient_ids with epilepsy (G40 group code), removing duplicates entries for same patient id :\n",
    "epilepsy_icd10_patient_ids = pd.DataFrame(epilepsy_icd10['PATIENT_NUM'].unique(), columns=['PATIENT_NUM'])\n",
    "\n",
    "epilepsy_icd10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14740839",
   "metadata": {},
   "source": [
    "Let's also isolate the demographic data for patients who have been diagnosed with epilepsy, and see how much of our dataset they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36785e1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.59 % of patients in our dataset have been diagnosed with an epilepsy ICD10 code.\n"
     ]
    }
   ],
   "source": [
    "# merging the patient id list with the demographic data\n",
    "\n",
    "epi_demo= pd.merge(epilepsy_icd10_patient_ids, demographics_joined, on='PATIENT_NUM', how='left')\n",
    "\n",
    "# calculating percentage of patients with epilepsy from total dataset cohort:\n",
    "\n",
    "epilepsy_percentage = (len(epilepsy_icd10_patient_ids) / data['PATIENT_NUM'].nunique()) * 100           \n",
    "print(round(epilepsy_percentage, 2), \"% of patients in our dataset have been diagnosed with an epilepsy ICD10 code.\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ebbc53",
   "metadata": {},
   "source": [
    "### Investigating the presence of other diagnostic codes in our defined patient cohort\n",
    "Our cohort consists of patients with epilepsy. \n",
    "Let's say we are interested in comparing which patients with epilepsy also have been diagnosed with migranes (ICD GROUP CODE G43).\n",
    "\n",
    "We create a variable called `icd` and set it to `G43`. (you can replace the ICD code \"G43\" with the ICD code of your choice in the next chunk of code)\n",
    "\n",
    "We then search for this ICD code of interest within our cohort. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4212407",
   "metadata": {},
   "outputs": [],
   "source": [
    "icd = 'G43'\n",
    "# first, creating a dataframe including only patients diagnosed with migraines (ICD GROUP CODE G43)\n",
    "\n",
    "migraines_icd10 = (diagnosis_icd10[diagnosis_icd10['TVAL_CHAR'].str.contains(icd)]\n",
    "                  .loc[:, ['PATIENT_NUM', 'CONCEPT_PATH', 'TVAL_CHAR']]\n",
    "                  .rename(columns={'TVAL_CHAR': 'icd_10_code'}))\n",
    "\n",
    "#next, extracting only the patient id's and removing any duplicates.\n",
    "migraines_icd10_patient_ids = pd.DataFrame(migraines_icd10['PATIENT_NUM'].unique(), columns=['PATIENT_NUM'])\n",
    "\n",
    "#finally, merging the epilepsy patient id list with the migraine patient id list to create a list including only patients that have both epilepsy and migraine ICD10 codes \n",
    "\n",
    "epilepsy_migraine= pd.merge(epilepsy_icd10_patient_ids, migraines_icd10_patient_ids, on='PATIENT_NUM', how='inner')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d15fef",
   "metadata": {},
   "source": [
    "Now, let's examine the original epilepsy dataframe and add a column indicating which patients also have migraines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b957d7e2",
   "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>PATIENT_NUM</th>\n",
       "      <th>migraines</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>000fae46-91b6-a373-1134-296eb55705e1</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>001ac6f0-8c11-79fc-8521-b004f687d67f</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>00444938-6d64-989b-b975-519e92b26171</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0223b8f5-ddf1-be38-64c5-4b69c5f311f4</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>0277b5f2-d7ed-0784-bcc6-39fa409f04fc</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</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>213</th>\n",
       "      <td>fcf38fba-9ee3-368a-1a4b-e7612779b579</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>ff40b184-64df-642f-193c-3d2e1ab7606b</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>ff78e9e4-6142-421b-e0e0-1be9289c7abb</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>ff82920b-399a-6785-1b66-8e5795631041</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>ffc5858e-0862-de1e-8075-e2f025746a4d</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              PATIENT_NUM  migraines   age     sex   race\n",
       "349  000fae46-91b6-a373-1134-296eb55705e1          0  69.0    Male  White\n",
       "203  001ac6f0-8c11-79fc-8521-b004f687d67f          1  55.0  Female  White\n",
       "318  00444938-6d64-989b-b975-519e92b26171          0   2.0  Female  White\n",
       "279  0223b8f5-ddf1-be38-64c5-4b69c5f311f4          0  47.0  Female  White\n",
       "328  0277b5f2-d7ed-0784-bcc6-39fa409f04fc          0  23.0  Female  White\n",
       "..                                    ...        ...   ...     ...    ...\n",
       "213  fcf38fba-9ee3-368a-1a4b-e7612779b579          0  27.0    Male  White\n",
       "268  ff40b184-64df-642f-193c-3d2e1ab7606b          0  56.0  Female  White\n",
       "113  ff78e9e4-6142-421b-e0e0-1be9289c7abb          0  16.0  Female  White\n",
       "145  ff82920b-399a-6785-1b66-8e5795631041          0  78.0    Male  White\n",
       "377  ffc5858e-0862-de1e-8075-e2f025746a4d          0   8.0  Female  White\n",
       "\n",
       "[418 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging the joint list to the original epilepsy patient list, and creating a migraine column indicating if the patient with epilepsy also has a migraine diagnosis (yes=1/ no=0):\n",
    "\n",
    "epilepsy_migraine['migraines'] = 1\n",
    "\n",
    "epilepsy_merged = (pd.merge(epilepsy_icd10_patient_ids, epilepsy_migraine, on='PATIENT_NUM', how='left')\n",
    "                   .fillna({'migraines': 0})\n",
    "                   .astype({'migraines': int})\n",
    "                   .merge(epi_demo, on='PATIENT_NUM', how='left')\n",
    "                   .sort_values(by='PATIENT_NUM'))\n",
    "\n",
    "epilepsy_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c647d55",
   "metadata": {},
   "source": [
    "The following table shows the percentage of patients with migraines in the epilepsy patient cohort.\n",
    "\n",
    "We can see that only a small percentage of our patients with epilepsy also experience migraines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45e6e1cb",
   "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>migraines</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>95.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   migraines  count  percentage\n",
       "0          0    398       95.22\n",
       "1          1     20        4.78"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the number and percentage of patient with migraines in the epilepsy patient cohort\n",
    "\n",
    "epi_mig_count = epilepsy_merged.groupby('migraines').size().reset_index(name='count')\n",
    "epi_mig_count['percentage'] = round((epi_mig_count['count'] / epi_mig_count['count'].sum()) * 100, 2)\n",
    "\n",
    "epi_mig_count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad7ab77",
   "metadata": {},
   "source": [
    "### Investigating what medications have been administered to our cohort of interest\n",
    "\n",
    "Let's now look into the medications data. We can isolate the medications data by searching the CONCEPT_PATH variable for the term `ACT Medications`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "758849f9",
   "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>PATIENT_NUM</th>\n",
       "      <th>CONCEPT_PATH</th>\n",
       "      <th>TVAL_CHAR</th>\n",
       "      <th>NVAL_NUM</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>VARIABLE_TYPE</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2933374</th>\n",
       "      <td>fe16b3d3-424a-86ce-00df-cd1386d7279f</td>\n",
       "      <td>\\ACT Medications\\I [Preparations]\\Ibuprofen\\Ibuprofen Oral Tablet\\Ibuprofen 100 Mg Oral Tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-08-10 01:36:06+00:00</td>\n",
       "      <td>ACT Medications</td>\n",
       "      <td>2021-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933375</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>\\ACT Medications\\D [Preparations]\\Diphenhydramine\\Diphenhydramine Oral Tablet\\Diphenhydramine Hydrochloride 25 Mg Oral Tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>1969-06-07 12:03:51+00:00</td>\n",
       "      <td>ACT Medications</td>\n",
       "      <td>1969-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933376</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>\\ACT Medications\\C [Preparations]\\Clopidogrel\\Clopidogrel Oral Tablet\\Clopidogrel 75 Mg Oral Tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2008-12-06 12:03:51+00:00</td>\n",
       "      <td>ACT Medications</td>\n",
       "      <td>2008-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933377</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>\\ACT Medications\\S [Preparations]\\Simvastatin\\Simvastatin Oral Tablet\\Simvastatin 20 Mg Oral Tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2008-12-06 12:03:51+00:00</td>\n",
       "      <td>ACT Medications</td>\n",
       "      <td>2008-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933378</th>\n",
       "      <td>2840480c-a43b-f0eb-916d-66aca598a621</td>\n",
       "      <td>\\ACT Medications\\M [Preparations]\\Metoprolol\\Metoprolol Extended Release Oral Tablet\\24 Hr Metoprolol Succinate 100 Mg Extended Release Oral Tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2008-12-06 12:03:51+00:00</td>\n",
       "      <td>ACT Medications</td>\n",
       "      <td>2008-12-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  PATIENT_NUM  \\\n",
       "2933374  fe16b3d3-424a-86ce-00df-cd1386d7279f   \n",
       "2933375  2840480c-a43b-f0eb-916d-66aca598a621   \n",
       "2933376  2840480c-a43b-f0eb-916d-66aca598a621   \n",
       "2933377  2840480c-a43b-f0eb-916d-66aca598a621   \n",
       "2933378  2840480c-a43b-f0eb-916d-66aca598a621   \n",
       "\n",
       "                                                                                                                                                CONCEPT_PATH  \\\n",
       "2933374                                                       \\ACT Medications\\I [Preparations]\\Ibuprofen\\Ibuprofen Oral Tablet\\Ibuprofen 100 Mg Oral Tablet   \n",
       "2933375                        \\ACT Medications\\D [Preparations]\\Diphenhydramine\\Diphenhydramine Oral Tablet\\Diphenhydramine Hydrochloride 25 Mg Oral Tablet   \n",
       "2933376                                                  \\ACT Medications\\C [Preparations]\\Clopidogrel\\Clopidogrel Oral Tablet\\Clopidogrel 75 Mg Oral Tablet   \n",
       "2933377                                                  \\ACT Medications\\S [Preparations]\\Simvastatin\\Simvastatin Oral Tablet\\Simvastatin 20 Mg Oral Tablet   \n",
       "2933378  \\ACT Medications\\M [Preparations]\\Metoprolol\\Metoprolol Extended Release Oral Tablet\\24 Hr Metoprolol Succinate 100 Mg Extended Release Oral Tablet   \n",
       "\n",
       "        TVAL_CHAR  NVAL_NUM                 TIMESTAMP    VARIABLE_TYPE  \\\n",
       "2933374       NaN       1.0 2021-08-10 01:36:06+00:00  ACT Medications   \n",
       "2933375       NaN     670.0 1969-06-07 12:03:51+00:00  ACT Medications   \n",
       "2933376       NaN     189.0 2008-12-06 12:03:51+00:00  ACT Medications   \n",
       "2933377       NaN     189.0 2008-12-06 12:03:51+00:00  ACT Medications   \n",
       "2933378       NaN     189.0 2008-12-06 12:03:51+00:00  ACT Medications   \n",
       "\n",
       "               DATE  \n",
       "2933374  2021-08-10  \n",
       "2933375  1969-06-07  \n",
       "2933376  2008-12-06  \n",
       "2933377  2008-12-06  \n",
       "2933378  2008-12-06  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a new dataframe containing only medication data:\n",
    "\n",
    "medications = data[data['CONCEPT_PATH'].str.contains(r'\\\\ACT Medications')]\n",
    "\n",
    "medications.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75e6ad8",
   "metadata": {},
   "source": [
    "For our epilepsy case study, we are interested in knowing whether a patient has been administered Carbamazepine or Pregabalin. \n",
    "- Carbamazepine is used to manage and treat epilepsy, trigeminal neuralgia, and acute manic and mixed episodes in bipolar I disorder.\n",
    "- Pregabalin treats nerve and muscle pain, including fibromyalgia. It can also treat seizures.\n",
    "\n",
    "\n",
    "We will search our dataset for these medications and isolate which patients are associated to those observations.\n",
    "\n",
    "We then isolate our results to our cohort of interest. \n",
    "\n",
    "The resulting table contains information on whether or not a patient who was diagnosed with epilepsy was ever administered carbamazepine or pregabalin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a079b7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_NUM</th>\n",
       "      <th>carbamazepine</th>\n",
       "      <th>pregabalin</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "                            PATIENT_NUM  carbamazepine  pregabalin\n",
       "0  e6ca8432-abc9-e3d4-4e5d-47f972f07181              0           0\n",
       "1  fc3d7a3a-b034-9727-8e4d-08c63d8b524e              0           0\n",
       "2  fb97ef69-a780-401d-cbb0-4e71b106f1be              0           0\n",
       "3  d0b49cc6-23d8-5d0e-8c67-f604d008b856              1           0\n",
       "4  e4e11faf-ef05-bcc7-245d-99d1ec8be5e2              0           0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering original data set to patients receiving Carbamazepine or/and Pregabalin\n",
    "\n",
    "carba_preg = (data[data['CONCEPT_PATH'].str.contains(r'Carbamazepine|Pregabalin')]\n",
    "              [['PATIENT_NUM', 'CONCEPT_PATH']])\n",
    "\n",
    "#creating a new column indicating if a patient has been prescribed Carbamazepine or Pregabalin (yes=1,no=0) for easier visualization and data manipulation \n",
    "carba_preg['carbamazepine'] = carba_preg['CONCEPT_PATH'].str.contains('Carbamazepine').astype(int)\n",
    "carba_preg['pregabalin'] = carba_preg['CONCEPT_PATH'].str.contains('Pregabalin').astype(int)\n",
    "\n",
    "carba_preg = carba_preg[['PATIENT_NUM', 'carbamazepine', 'pregabalin']].drop_duplicates()\n",
    "\n",
    "# merging the patient id list of epilepsy patients with the patient medication data frame, filing missing entries with \"0\"(=no/ no information included in dataset):\n",
    "\n",
    "patient_icd10_med = (\n",
    "    pd.merge(epilepsy_icd10_patient_ids, carba_preg, on='PATIENT_NUM', how='left')\n",
    "    .fillna(0)\n",
    "    .astype({'carbamazepine': 'int', 'pregabalin': 'int'})\n",
    ")\n",
    "\n",
    "\n",
    "patient_icd10_med.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5d2a1e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122 patient(s) with epilepsy were taking carbamazepine\n",
      "1 patient(s) with epilepsy were taking pregabalin\n"
     ]
    }
   ],
   "source": [
    "print(patient_icd10_med['carbamazepine'].value_counts()[1], \"patient(s) with epilepsy were taking carbamazepine\")\n",
    "print(patient_icd10_med['pregabalin'].value_counts()[1], \"patient(s) with epilepsy were taking pregabalin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcf39ac",
   "metadata": {},
   "source": [
    "### Invsetigating lab results over time for our cohort of interest\n",
    "\n",
    "We can isolate the lab related information by searching for 'ACT Lab Test Results' in the CONCEPT_PATH column.\n",
    "\n",
    "Below is a preview of the labs available in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9db402d0",
   "metadata": {
    "scrolled": true
   },
   "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>CONCEPT_PATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\\ACT Lab Test Results\\Chemistry\\Electrolytes\\Bicarbonate\\Carbon dioxide in Serum or Plasma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\ACT Lab Test Results\\Chemistry\\Electrolytes\\Calcium\\Calcium in Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\ACT Lab Test Results\\Chemistry\\Electrolytes\\Calcium\\Calcium in Serum or Plasma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\ACT Lab Test Results\\Chemistry\\Electrolytes\\Chloride\\Chloride in Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\\ACT Lab Test Results\\Chemistry\\Electrolytes\\Chloride\\Chloride in Serum or Plasma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                 CONCEPT_PATH\n",
       "0  \\ACT Lab Test Results\\Chemistry\\Electrolytes\\Bicarbonate\\Carbon dioxide in Serum or Plasma\n",
       "1                       \\ACT Lab Test Results\\Chemistry\\Electrolytes\\Calcium\\Calcium in Blood\n",
       "2             \\ACT Lab Test Results\\Chemistry\\Electrolytes\\Calcium\\Calcium in Serum or Plasma\n",
       "3                     \\ACT Lab Test Results\\Chemistry\\Electrolytes\\Chloride\\Chloride in Blood\n",
       "4           \\ACT Lab Test Results\\Chemistry\\Electrolytes\\Chloride\\Chloride in Serum or Plasma"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new dataframe containing only the lab test results for all patients in the original dataset:\n",
    "lab_test_results = data[data['CONCEPT_PATH'].str.contains(r'\\\\ACT Lab Test Results')]\n",
    "\n",
    "# creating a list with unique types of lab tests included in the dataset:\n",
    "\n",
    "lab_list = (lab_test_results[['CONCEPT_PATH']]\n",
    "                   .drop_duplicates()\n",
    "                   .sort_values(by='CONCEPT_PATH')\n",
    "                   .reset_index(drop=True))\n",
    "\n",
    "lab_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bf8737",
   "metadata": {},
   "source": [
    "In the following example, we will focus on AST (aspartate aminotransferase) and ALT (alanine transaminase) lab test results. \n",
    "We will also only focus on lab results from 2023. \n",
    "- AST is an enzyme found in the liver, muscles, and other organs. When cells that contain AST are damaged, they release AST into the blood\n",
    "- An ALT blood test is used to diagnose liver disorders. ALT is a liver enzyme that is released in the blood when liver cells are damaged.\n",
    "\n",
    "(you can replace \"ALT\" or \"AST\" with your choice of lab test, to add additional lab tests just add a \"|\" before the next lab test name)\n",
    "\n",
    "\n",
    "*Note that the number of lab result entries can vary widely per patient, and even multiple entries per day are possible. In the graphic representation and statistical measurement tables, each individual lab result is treated as an individual data point, without the use of weighted averages or other methodological statistical adjustments.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "967c6add",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_NUM</th>\n",
       "      <th>lab_test</th>\n",
       "      <th>value</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1915668</th>\n",
       "      <td>0015fb36-0cad-3a48-87d9-a0f61c12c80d</td>\n",
       "      <td>alt</td>\n",
       "      <td>25.2</td>\n",
       "      <td>2023-12-23 02:34:46+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915669</th>\n",
       "      <td>0015fb36-0cad-3a48-87d9-a0f61c12c80d</td>\n",
       "      <td>ast</td>\n",
       "      <td>9.1</td>\n",
       "      <td>2023-12-23 02:34:46+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477610</th>\n",
       "      <td>00bcc24e-4602-a868-1ce2-947f1ec1c00f</td>\n",
       "      <td>alt</td>\n",
       "      <td>33.8</td>\n",
       "      <td>2023-10-04 22:16:14+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477611</th>\n",
       "      <td>00bcc24e-4602-a868-1ce2-947f1ec1c00f</td>\n",
       "      <td>ast</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2023-10-04 22:16:14+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252599</th>\n",
       "      <td>00d75a34-3344-8c62-401b-eb1fc27a4ba5</td>\n",
       "      <td>alt</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2023-12-08 05:32:11+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  PATIENT_NUM lab_test  value  \\\n",
       "1915668  0015fb36-0cad-3a48-87d9-a0f61c12c80d      alt   25.2   \n",
       "1915669  0015fb36-0cad-3a48-87d9-a0f61c12c80d      ast    9.1   \n",
       "2477610  00bcc24e-4602-a868-1ce2-947f1ec1c00f      alt   33.8   \n",
       "2477611  00bcc24e-4602-a868-1ce2-947f1ec1c00f      ast   18.2   \n",
       "2252599  00d75a34-3344-8c62-401b-eb1fc27a4ba5      alt   30.1   \n",
       "\n",
       "                        TIMESTAMP  \n",
       "1915668 2023-12-23 02:34:46+00:00  \n",
       "1915669 2023-12-23 02:34:46+00:00  \n",
       "2477610 2023-10-04 22:16:14+00:00  \n",
       "2477611 2023-10-04 22:16:14+00:00  \n",
       "2252599 2023-12-08 05:32:11+00:00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering the original dataset to include only AST and ALT test results from the year 2023 and cleaning duplicates:\n",
    "alt_ast = lab_test_results[lab_test_results['CONCEPT_PATH'].str.contains('ALT|AST')]\n",
    "alt_ast = alt_ast[alt_ast['TIMESTAMP'] >= pd.Timestamp('2023-01-01', tz='UTC')]\n",
    "alt_ast = alt_ast[alt_ast['TIMESTAMP'] <= pd.Timestamp('2023-12-31', tz='UTC')]\n",
    "\n",
    "\n",
    "\n",
    "alt_ast['lab_test'] = np.where(\n",
    "    alt_ast['CONCEPT_PATH'].str.contains('AST'), 'ast',\n",
    "    np.where(alt_ast['CONCEPT_PATH'].str.contains('ALT'), 'alt', None)\n",
    ")\n",
    "\n",
    "\n",
    "alt_ast = (\n",
    "    alt_ast\n",
    "    .rename(columns={'NVAL_NUM': 'value'})\n",
    "    .loc[:, ['PATIENT_NUM', 'lab_test', 'value', 'TIMESTAMP']]\n",
    "    .sort_values(by=['PATIENT_NUM', 'TIMESTAMP'])\n",
    "    .drop_duplicates()\n",
    ")\n",
    "\n",
    "alt_ast.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe62132",
   "metadata": {},
   "source": [
    "Let's now graph a distribution of ALT and AST values in 2023."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c0dfd99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9145/2280149245.py:5: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.boxplot(x='lab_test', y='value', data=alt_ast, palette={'ast': 'skyblue', 'alt': 'red'})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a box plot graph of the results. \n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.boxplot(x='lab_test', y='value', data=alt_ast, palette={'ast': 'skyblue', 'alt': 'red'})\n",
    "plt.title('Liver Transaminase Levels in 2023, n=' + str(len(alt_ast)))\n",
    "plt.xlabel('Lab Test')\n",
    "plt.ylabel('Value (u/l)')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0fe3aec",
   "metadata": {},
   "source": [
    "Let's say we are interested in a specific patient with epilepsy. \n",
    "Below, we plot the AST and ALT lab trends over the year 2023 for patient id # 1f9e58a1-0b6f-7f53-1495-8b05b69ad4ed.\n",
    "\n",
    "You can modify this code for any patient of interest by changing the `patient_of_interest` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02c6d2cd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_of_interest = \"1f9e58a1-0b6f-7f53-1495-8b05b69ad4ed\"\n",
    "\n",
    "#filtering for the specific patient's AST and ALT levels over time using the 2023 alt/ast dataframe we previously created:\n",
    "single_patient_data = alt_ast[alt_ast['PATIENT_NUM'] == patient_of_interest]\n",
    "\n",
    "# Pivot the data so that each lab test becomes a separate column\n",
    "patient_data_pivot = single_patient_data.pivot(index='TIMESTAMP', columns='lab_test', values='value')\n",
    "\n",
    "#creaing a plot showing the results over time:\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(patient_data_pivot.index, patient_data_pivot['alt'], marker='o', linestyle='-', color='r', label='alt')\n",
    "plt.plot(patient_data_pivot.index, patient_data_pivot['ast'], marker='o', linestyle='-', color='b', label='ast')\n",
    "plt.title('Liver transaminase levels over time for patient ' +  str(patient_of_interest) + ' in 2023')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value (u/l)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc7ea78",
   "metadata": {},
   "source": [
    "## Investigating procedural data over time. Case study: Appendectomy.\n",
    "\n",
    "Finally, let's take a look at the procedures data. \n",
    "\n",
    "The procedures are defined by CPT codes. \n",
    "We will explore the procedures in this data set and look at specific procedures of interest in a given time frame.\n",
    "\n",
    "We can isolate the procedure related information by searching for 'ACT Procedures' in the CONCEPT_PATH column.\n",
    "\n",
    "Below is an example of the procedures documented in this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "741f166c",
   "metadata": {
    "scrolled": true
   },
   "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>CONCEPT_PATH</th>\n",
       "      <th>procedure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3682095</th>\n",
       "      <td>\\ACT Procedures CPT\\Radiology Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures of the Chest</td>\n",
       "      <td>Radiologic examination, chest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682096</th>\n",
       "      <td>\\ACT Procedures CPT\\Radiology Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures of the Chest</td>\n",
       "      <td>Radiologic examination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682097</th>\n",
       "      <td>\\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Digestive System\\Surgical Procedures on the Colon and Rectum</td>\n",
       "      <td>Endoscopy Procedures on the Rectum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682104</th>\n",
       "      <td>\\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Integumentary System\\Surgical Procedures on the Breast\\Excision Procedures on the Breast</td>\n",
       "      <td>Biopsy of breast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682106</th>\n",
       "      <td>\\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Female Genital System\\Surgical Procedures on the Corpus Uteri\\Introduction Procedures on the Corpus Uteri</td>\n",
       "      <td>Insertion of intrauterine device (IUD)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                             CONCEPT_PATH  \\\n",
       "3682095   \\ACT Procedures CPT\\Radiology Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures of the Chest   \n",
       "3682096   \\ACT Procedures CPT\\Radiology Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures\\Diagnostic Radiology (Diagnostic Imaging) Procedures of the Chest   \n",
       "3682097                                               \\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Digestive System\\Surgical Procedures on the Colon and Rectum   \n",
       "3682104                   \\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Integumentary System\\Surgical Procedures on the Breast\\Excision Procedures on the Breast   \n",
       "3682106  \\ACT Procedures CPT\\Surgery\\Surgical Procedures on the Female Genital System\\Surgical Procedures on the Corpus Uteri\\Introduction Procedures on the Corpus Uteri   \n",
       "\n",
       "                                      procedure  \n",
       "3682095           Radiologic examination, chest  \n",
       "3682096                  Radiologic examination  \n",
       "3682097      Endoscopy Procedures on the Rectum  \n",
       "3682104                        Biopsy of breast  \n",
       "3682106  Insertion of intrauterine device (IUD)  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a list with unique types of procedures included in the dataset :\n",
    "\n",
    "procedures = (\n",
    "    data[data['CONCEPT_PATH'].str.contains(r'\\\\ACT Procedures')]\n",
    "    .loc[:, ['CONCEPT_PATH', 'TVAL_CHAR']]\n",
    "    .drop_duplicates()\n",
    "    .rename(columns={'TVAL_CHAR': 'procedure'})\n",
    ")\n",
    "\n",
    "procedures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a52f1c",
   "metadata": {},
   "source": [
    "Below, we filter the procedural data to identify a cohort of patients who have undergone an appendectomy since the beginning of 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1cc76554",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>PATIENT_NUM</th>\n",
       "      <th>procedure</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>VARIABLE_TYPE</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3683372</th>\n",
       "      <td>5264d991-9864-e84e-b5d0-1da3f3e611b2</td>\n",
       "      <td>Appendectomy</td>\n",
       "      <td>2020-11-18 03:26:18+00:00</td>\n",
       "      <td>ACT Procedures CPT</td>\n",
       "      <td>2020-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3687591</th>\n",
       "      <td>034cf0cf-3901-3e09-e7ce-f135e68563f2</td>\n",
       "      <td>Appendectomy</td>\n",
       "      <td>2021-11-18 16:45:31+00:00</td>\n",
       "      <td>ACT Procedures CPT</td>\n",
       "      <td>2021-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3690114</th>\n",
       "      <td>899a5c0c-7c5f-4110-a59a-269bfda3c4d2</td>\n",
       "      <td>Appendectomy</td>\n",
       "      <td>2022-11-28 06:06:57+00:00</td>\n",
       "      <td>ACT Procedures CPT</td>\n",
       "      <td>2022-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3690148</th>\n",
       "      <td>a417c4de-60c5-dd3d-2ee3-07409ab84707</td>\n",
       "      <td>Appendectomy</td>\n",
       "      <td>2021-08-31 10:33:58+00:00</td>\n",
       "      <td>ACT Procedures CPT</td>\n",
       "      <td>2021-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3691958</th>\n",
       "      <td>d0598615-23c8-74f0-ff8f-36a21755a0cc</td>\n",
       "      <td>Appendectomy</td>\n",
       "      <td>2020-10-17 10:23:26+00:00</td>\n",
       "      <td>ACT Procedures CPT</td>\n",
       "      <td>2020-10-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  PATIENT_NUM     procedure  \\\n",
       "3683372  5264d991-9864-e84e-b5d0-1da3f3e611b2  Appendectomy   \n",
       "3687591  034cf0cf-3901-3e09-e7ce-f135e68563f2  Appendectomy   \n",
       "3690114  899a5c0c-7c5f-4110-a59a-269bfda3c4d2  Appendectomy   \n",
       "3690148  a417c4de-60c5-dd3d-2ee3-07409ab84707  Appendectomy   \n",
       "3691958  d0598615-23c8-74f0-ff8f-36a21755a0cc  Appendectomy   \n",
       "\n",
       "                        TIMESTAMP       VARIABLE_TYPE        DATE  \n",
       "3683372 2020-11-18 03:26:18+00:00  ACT Procedures CPT  2020-11-18  \n",
       "3687591 2021-11-18 16:45:31+00:00  ACT Procedures CPT  2021-11-18  \n",
       "3690114 2022-11-28 06:06:57+00:00  ACT Procedures CPT  2022-11-28  \n",
       "3690148 2021-08-31 10:33:58+00:00  ACT Procedures CPT  2021-08-31  \n",
       "3691958 2020-10-17 10:23:26+00:00  ACT Procedures CPT  2020-10-17  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering to create a new dataframe including patients with a history of an appendectomy procedure\n",
    "\n",
    "appendectomy = (\n",
    "    data[data['TVAL_CHAR'].str.contains('Appendectomy',na=False)]\n",
    "    .rename(columns={'TVAL_CHAR': 'procedure'})\n",
    "    .drop(columns=['CONCEPT_PATH', 'NVAL_NUM'])\n",
    "    .drop_duplicates()\n",
    ")\n",
    "\n",
    "# filtering to only include patients who had an appendectomy since the beginning of the year 2020\n",
    "\n",
    "appendectomy_2020 = appendectomy[appendectomy['TIMESTAMP'] >= pd.Timestamp('2020-01-01', tz='UTC')]\n",
    "\n",
    "appendectomy_2020.head()"
   ]
  }
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