189 lines (188 with data), 5.4 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "import scipy\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## This notebook assumes: \n",
    "#1: The loaded outcome is if the outcome happens ever, as opposed to the other evaluation, which was focused on the first 5 days\n",
    "#2: num_windows is the number of hours // 4 in which we want to make the triaging decision. Our default is making predictions using 48 hours of data to triage\n",
    "\n",
    "num_windows = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load models\n",
    "\n",
    "models_dict = joblib.load('models_dict.joblib')\n",
    "models_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read in sample cohort\n",
    "# Cohort should include those who did not have outcome within the first two days\n",
    "# Each individual should have exactly 12 windows\n",
    "# The outcome shoudl be whether deterioation occurred ever (not just within the first five days)\n",
    "\n",
    "df_cohort = pd.read_csv('sample_cohort_outcome_ever_past_2days.csv')\n",
    "\n",
    "# Remove windows after 2 days\n",
    "df_cohort = df_cohort[df_cohort['window_id'] < num_windows]\n",
    "\n",
    "# Remove incomplete windows\n",
    "df_cohort = df_cohort[df_cohort['window_id'] >= 1]\n",
    "\n",
    "test_hosp, test_window, test_y = df_cohort['hosp_id'], df_cohort['window_id'], df_cohort['y']\n",
    "\n",
    "cohort_IDs = df_cohort.set_index('ID')[[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(np.unique(test_hosp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## M-CURES Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcures_clfs = models_dict['M-CURES']\n",
    "df_mcures = pd.read_csv('../preprocessing/sample_output/mcures.csv').set_index('ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate aggregated scores for all examples\n",
    "\n",
    "eval_matrix = scipy.sparse.csr_matrix(cohort_IDs.join(df_mcures).values.astype(float))\n",
    "all_y = np.array([clf.predict_proba(eval_matrix)[:,1] for clf in mcures_clfs])\n",
    "y_scores = all_y.mean(0)\n",
    "\n",
    "df_Yte_all = pd.DataFrame({'hosp_id': test_hosp, 'window_id': test_window, 'y': test_y, 'y_score': y_scores})\n",
    "df_Yte_agg = df_Yte_all.groupby('hosp_id').mean() #Can be changed to max, depending on how you want to aggregate scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.sort(df_Yte_agg['y_score'])\n",
    "total_negs = df_Yte_agg['y']\n",
    "for s in scores: \n",
    "    curr = df_Yte_agg[df_Yte_agg['y_score'] <= s]\n",
    "    # How many people do we correctly flag with atleast an NPV of 0.95 (i.e. At most 5% of people we flagged have the event)\n",
    "    if 1 - curr['y'].mean() == 0.95: \n",
    "        curr_no_outcome = curr[curr['y'] == 0]\n",
    "        print('NPV: {:.2f}, Population % Flagged Correctly as Low-Risk {:.2%}'.format(1 - curr['y'].mean(), curr_no_outcome.shape[0] / len(scores)))\n",
    "        latest = curr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sweep over NPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the percentage of correctly flagged low-risk patients (true negatives) as NPV varies\n",
    "scores = np.sort(df_Yte_agg['y_score'])\n",
    "mcures_npvs = []\n",
    "mcures_flagged = []\n",
    "\n",
    "for s in scores: \n",
    "    curr = df_Yte_agg[df_Yte_agg['y_score'] <= s]\n",
    "    curr_no_outcome = curr[curr['y'] == 0]\n",
    "    mcures_npvs.append(1 - curr['y'].mean())\n",
    "    mcures_flagged.append(curr_no_outcome.shape[0] / len(scores))\n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n",
    "\n",
    "plt.plot(mcures_flagged, mcures_npvs, label = 'M-CURES Model', lw = 1.25)\n",
    "\n",
    "plt.xlabel('Percentage Correctly Flagged as Low-Risk')\n",
    "plt.ylabel('Negative Predictive Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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