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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parse out medications from discharge summaries in MIMIC "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import psycopg2\n",
"import getpass\n",
"import re\n",
"\n",
"# Import the finddrugs module\n",
"import finddrugs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a database connection\n",
"user = 'postgres'\n",
"host = 'localhost'\n",
"dbname = 'mimic'\n",
"schema = 'mimiciii'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Connect to the database\n",
"con = psycopg2.connect(dbname=dbname, user=user, host=host, \n",
" password=getpass.getpass(prompt='Password:'.format(user)))\n",
"cur = con.cursor()\n",
"cur.execute('SET search_path to {}'.format(schema))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get the notes to be analysed\n",
"# NOTE: we are limiting the query to 100 rows here\n",
"query = \\\n",
"\"\"\"\n",
"SELECT n.row_id, n.subject_id, n.hadm_id, n.category, n.description, n.text\n",
"FROM noteevents n\n",
"WHERE n.category like 'Discharge summary'\n",
"LIMIT 100;\n",
"\"\"\"\n",
"\n",
"data = pd.read_sql_query(query,con)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Search the notes\n",
"finddrugs.search(data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>ROW_ID</th>\n",
" <th>SUBJECT_ID</th>\n",
" <th>HADM_ID</th>\n",
" <th>HIST_FOUND</th>\n",
" <th>DEPRESSION</th>\n",
" <th>ADMIT_FOUND</th>\n",
" <th>DIS_FOUND</th>\n",
" <th>GEN_DEPRESS_MEDS_FOUND</th>\n",
" <th>GROUP</th>\n",
" <th>SSRI</th>\n",
" <th>MISC</th>\n",
" <th>citalopram</th>\n",
" <th>escitalopram</th>\n",
" <th>fluoxetine</th>\n",
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],
"text/plain": [
" ROW_ID SUBJECT_ID HADM_ID HIST_FOUND DEPRESSION ADMIT_FOUND \\\n",
"0 174 22532 167853 0 0 0 \n",
"1 175 13702 107527 1 0 1 \n",
"2 176 13702 167118 1 1 1 \n",
"3 177 13702 196489 1 1 1 \n",
"4 178 26880 135453 1 0 1 \n",
"\n",
" DIS_FOUND GEN_DEPRESS_MEDS_FOUND GROUP SSRI MISC citalopram \\\n",
"0 0 0 0 0 0 0 \n",
"1 1 0 3 1 0 0 \n",
"2 1 0 3 1 0 0 \n",
"3 1 0 3 1 0 0 \n",
"4 1 0 3 1 0 1 \n",
"\n",
" escitalopram fluoxetine fluvoxamine paroxetine sertraline \n",
"0 0 0 0 0 0 \n",
"1 1 0 0 0 0 \n",
"2 1 0 0 0 0 \n",
"3 1 0 0 0 0 \n",
"4 0 0 0 0 0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load the output to a dataframe\n",
"medications = pd.read_csv('output.csv')\n",
"medications.head()"
]
}
],
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