time_study_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Time Periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook visualizes the notes over time plotted against important periods during the adulterated heparin crisis.\n",
    "+ python = 3.7.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "import time\n",
    "import os\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import sys\n",
    "\n",
    "from importlib_metadata import version\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import psycopg2\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PASSWORD = os.environ.get(\"PASSWORD\")\n",
    "USERNAME = os.environ.get(\"USERNAME\")\n",
    "POSTGRES_CONNECT = os.environ.get(\"POSTGRES_CONNECT\")\n",
    "POSTGRES_ENGINE = os.environ.get(\"POSTGRES_ENGINE\")\n",
    "\n",
    "conn = psycopg2.connect(POSTGRES_CONNECT)\n",
    "\n",
    "cur = conn.cursor();\n",
    "cur.execute(\"\"\"SET search_path = mimiciii;\"\"\")\n",
    "\n",
    "engine = create_engine(POSTGRES_ENGINE)#'postgresql://postgres:postgres@localhost/MIMIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "libraries = ['pandas','sqlalchemy','psycopg2','tqdm','scipy','numpy','matplotlib','scikit-learn']\n",
    "print('last ran: ',datetime.now() )\n",
    "print(\"Python Version:\", sys.version[0:7])\n",
    "print( \"operating system:\", sys.platform)\n",
    "\n",
    "for lib in libraries:\n",
    "    print(lib + ' version: ' + version(lib))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_sql(\"\"\"select * from mimiciii.time_study_notes\"\"\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Time Periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting total number of admissions over time against key dates during the adulterated heparin crisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "times = df.set_index('true_admittime').resample('M').count()\n",
    "times.plot(style='-', figsize=(16,6), c='k', legend=None)\n",
    "plt.axvline(x='2006-06', c='r') # pig disease discovered\n",
    "plt.axvline(x='2007-11', c='r') # CDC study of patients getting sick (earliest cases)\n",
    "plt.axvline(x='2008-01', c='b', linestyle=':') # Baxter issues first heparin recall\n",
    "plt.axvline(x='2008-05', c='b', linestyle=':') # final heparin recall completed\n",
    "plt.title('Admissions Over Period of Interest');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Proportion of Time Periods\n",
    "labels = 'Period A', 'Period B', 'Period C'\n",
    "pre = df[(df['true_admittime'] < '2006-06-01')].shape[0]\n",
    "during = df[(df['true_admittime'] > '2006-06-01') & (df['true_admittime'] < '2007-11-01')].shape[0]\n",
    "post = df[(df['true_admittime'] > '2007-11-01')].shape[0]\n",
    "sizes = [pre, during, post]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  \n",
    "ax1.set_title('Proportion of Classes')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = df.set_index('true_admittime').resample('Q').count()\n",
    "times.plot(style='-', figsize=(16,6), c='k', legend=None)\n",
    "plt.axvline(x='2006-06', c='r') # pig disease discovered\n",
    "plt.axvline(x='2007-11', c='r') # CDC study of patients getting sick (earliest cases)\n",
    "plt.axvline(x='2008-01', c='b', linestyle=':') # Baxter issues first heparin recall\n",
    "plt.axvline(x='2008-05', c='b', linestyle=':') # final heparin recall completed\n",
    "plt.title('Admissions Over Period of Interest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime('2008-07-01T00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = []\n",
    "for i in df['true_admittime']:\n",
    "    if i < pd.to_datetime('2008-07-01T00') and i >= pd.to_datetime('2007-07-01T00'):\n",
    "        periods.append('2007_2008')\n",
    "    elif i < pd.to_datetime('2007-07-01T00') and i >= pd.to_datetime('2006-07-01T00'):\n",
    "        periods.append('2006_2007')\n",
    "    elif i < pd.to_datetime('2006-07-01T00') and i >= pd.to_datetime('2005-07-01T00'):\n",
    "        periods.append('2005_2006')\n",
    "    elif i < pd.to_datetime('2005-07-01T00') and i >= pd.to_datetime('2004-07-01T00'):\n",
    "        periods.append('2004_2005')\n",
    "    elif i < pd.to_datetime('2004-07-01T00') and i >= pd.to_datetime('2003-07-01T00'):\n",
    "        periods.append('2003_2004')\n",
    "    elif i < pd.to_datetime('2003-07-01T00') and i >= pd.to_datetime('2002-07-01T00'):\n",
    "        periods.append('2002_2003')\n",
    "    elif i < pd.to_datetime('2002-07-01T00') and i >= pd.to_datetime('2001-07-01T00'):\n",
    "        periods.append('2001_2002')\n",
    "    elif i < pd.to_datetime('2001-07-01T00') and i >= pd.to_datetime('2000-07-01T00'):\n",
    "        periods.append('2000_2001')\n",
    "    elif i < pd.to_datetime('2009-07-01T00') and i >= pd.to_datetime('2008-07-01T00'):\n",
    "        periods.append('2008_2009')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time_period'] = periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = sns.countplot(df['time_period'],\n",
    "                     color = 'gray')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=45)\n",
    "chart.set_title('Admissions Per Time Period')\n",
    "chart.set(xlabel='Time Period (July 1 - June 30 Cycle)', ylabel='Number Admissions');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time_period'].value_counts()"
   ]
  }
 ],
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