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+{
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "![Urgences - Image CC0 - pexels.com](img/pexels-pixabay-263402.jpg \"Urgences\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "# Challenge - [ED Lab Prediction]\n",
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+ "_Nom à trouver_"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## Objectif"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "Ce notebook récupère la classifications des médicaments (ATC-IV) à partir des API publiques de la `national library of medecine`."
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "```\n",
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+ "\n",
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+ " This product uses publicly available data from the U.S. National Library of Medicine (NLM), National Institutes of Health, Department of Health and Human Services; NLM is not responsible for the product and does not endorse or recommend this or any other product.\n",
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+ "\n",
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+ "```"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "# Récupération des données"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 16,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import sqlite3\n",
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+ "import pandas as pd\n",
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+ "from xml.etree import ElementTree as ET"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 12,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "conn = sqlite3.connect(\"./data/mimic-iv.sqlite\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 284,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Liste des traitements\n",
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+ "drugs_gsn = pd.read_sql(\"\"\"\n",
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+ " SELECT gsn, ndc, name\n",
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+ " FROM medrecon\n",
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+ " GROUP BY gsn\n",
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+ "\"\"\", conn)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 223,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Création de la liste pour utilisation de l'outils RxMix\n",
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+ "# https://mor.nlm.nih.gov/RxMix/\n",
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+ "\n",
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+ "## Utilisation de RxMix pour ne pas sur-solliciter les serveurs de la NML à travers des queries unitaires\n",
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+ "\n",
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+ "drugs_gsn[\"gsn\"].to_csv(\"./config/gsn_for_batch.txt\", header=False, index=False)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 263,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Récupération des classes ATC associées\n",
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+ "atccode = ET.parse(\"./config/atccode.xml\")\n",
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+ "\n",
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+ "atccode_parsed = [\n",
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+ " (int(x[0].text), \n",
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+ " [\n",
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+ " y.text \n",
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+ " for y in x[2].findall(\".//classId\") \n",
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+ " if y.text not in [\"-\",\"\"]\n",
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+ " ]) for x in atccode.getroot() if len(x) > 2\n",
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+ "]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 267,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Identification de 5906 sur 9262 (0.64) code NDC avec un code rxCUI associé.\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "n_found = len([x for x in atccode_parsed if len(x[1])>0])\n",
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+ "n_total = drugs_gsn.shape[0]\n",
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+ "\n",
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+ "print(f\"Identification de {n_found} sur {n_total} ({n_found/n_total:.2f}) code NDC avec un code rxCUI associé.\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 272,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "atccode_parsed_filtered = dict([x for x in atccode_parsed if len(x[1])>0])\n",
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+ "atccode_parsed_filtered_df = pd.DataFrame.from_dict(atccode_parsed_filtered, orient=\"index\") \\\n",
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+ " .apply(lambda x: x.dropna().tolist(), axis=1) \\\n",
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+ " .reset_index() \\\n",
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+ " .rename(columns={\"index\":\"gsn\", 0:\"atc\"})\n",
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+ "\n",
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+ "# Réunion de NDC et ATC\n",
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+ "drugs_atc = atccode_parsed_filtered_df \\\n",
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+ " .explode(\"atc\") \\\n",
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+ " .dropna() \\\n",
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+ " .drop_duplicates([\"gsn\",\"atc\"])[[\"gsn\",\"atc\"]]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 296,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "drugs_atc.to_csv(\"./config/atc_items.csv\", index=False)"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "interpreter": {
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+ "hash": "28b293e0c0671e44c7281dde6399c7c7419d3faca031d22494da8635907ada72"
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+ },
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+ "kernelspec": {
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+ "display_name": "Python 3.9.7 ('base')",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.9.7"
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+ },
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+ "orig_nbformat": 4
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+}
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