{ "cells": [ { "cell_type": "markdown", "id": "1b43c8f1", "metadata": {}, "source": [ "# Find similar patients who are treated differently within the same hospital" ] }, { "cell_type": "markdown", "id": "97e262a5", "metadata": {}, "source": [ "## Aims\n", "\n", "- Investigate the number of misclassifications within a hospital\n", "- Define a similarity measure using the decision tree structure of a random forest classifier\n", "- For patients with a predicted outcome different to their true outcome, find similar patients that were treated differently" ] }, { "cell_type": "markdown", "id": "324b703c", "metadata": {}, "source": [ "## Code " ] }, { "cell_type": "markdown", "id": "27d42f11", "metadata": {}, "source": [ "### Import libraries " ] }, { "cell_type": "code", "execution_count": 1, "id": "3ef6869b", "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", "import pickle as pkl\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as ticker\n", "%matplotlib inline\n", "\n", "import sklearn as sk\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# Turn warnings off to keep notebook tidy\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "fce0441a", "metadata": {}, "source": [ "### Load pre-trained hospital models into dictionary `hospital2model` \n", "\n", "keys = hospitals\n", "\n", "values = trained_classifier, threshold, patients, outcomes\n", "\n", "Note: patients is a numpy array. " ] }, { "cell_type": "code", "execution_count": 2, "id": "f6e16911", "metadata": {}, "outputs": [], "source": [ "with open ('./models/trained_hospital_models_for _cohort.pkl', 'rb') as f:\n", " \n", " hospital2model = pkl.load(f)" ] }, { "cell_type": "markdown", "id": "5d976937", "metadata": {}, "source": [ "### Select a hospital for investigation " ] }, { "cell_type": "code", "execution_count": 3, "id": "4f4bb860", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'TPFFP4410O'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hospital = list(hospital2model.keys())[1]\n", "hospital" ] }, { "cell_type": "markdown", "id": "0d8bc208", "metadata": {}, "source": [ "### Load test cohort and extract hospital patients " ] }, { "cell_type": "code", "execution_count": 4, "id": "e13c8f74", "metadata": {}, "outputs": [], "source": [ "cohort = pd.read_csv('../data/10k_training_test/cohort_10000_test.csv')\n", "\n", "test_patients_df = cohort.loc[cohort['StrokeTeam']==hospital]" ] }, { "cell_type": "code", "execution_count": 5, "id": "76cced3b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StrokeTeamS1AgeOnArrivalS1OnsetToArrival_minS2RankinBeforeStrokeLocLocQuestionsLocCommandsBestGazeVisualFacialPalsy...S2NewAFDiagnosis_YesS2NewAFDiagnosis_missingS2StrokeType_InfarctionS2StrokeType_Primary Intracerebral HaemorrhageS2StrokeType_missingS2TIAInLastMonth_NoS2TIAInLastMonth_No butS2TIAInLastMonth_YesS2TIAInLastMonth_missingS2Thrombolysis
105TPFFP4410O72.568.0222.02.00.02.02.0...0101000010
720TPFFP4410O87.5108.0032.02.02.03.03.0...0101000010
759TPFFP4410O82.5131.0412.02.00.00.01.0...0010000010
779TPFFP4410O57.5145.0000.00.00.00.02.0...0110000011
898TPFFP4410O67.5162.0100.00.01.02.03.0...0010000011
\n", "

5 rows × 101 columns

\n", "
" ], "text/plain": [ " StrokeTeam S1AgeOnArrival S1OnsetToArrival_min S2RankinBeforeStroke \\\n", "105 TPFFP4410O 72.5 68.0 2 \n", "720 TPFFP4410O 87.5 108.0 0 \n", "759 TPFFP4410O 82.5 131.0 4 \n", "779 TPFFP4410O 57.5 145.0 0 \n", "898 TPFFP4410O 67.5 162.0 1 \n", "\n", " Loc LocQuestions LocCommands BestGaze Visual FacialPalsy ... \\\n", "105 2 2.0 2.0 0.0 2.0 2.0 ... \n", "720 3 2.0 2.0 2.0 3.0 3.0 ... \n", "759 1 2.0 2.0 0.0 0.0 1.0 ... \n", "779 0 0.0 0.0 0.0 0.0 2.0 ... \n", "898 0 0.0 0.0 1.0 2.0 3.0 ... \n", "\n", " S2NewAFDiagnosis_Yes S2NewAFDiagnosis_missing S2StrokeType_Infarction \\\n", "105 0 1 0 \n", "720 0 1 0 \n", "759 0 0 1 \n", "779 0 1 1 \n", "898 0 0 1 \n", "\n", " S2StrokeType_Primary Intracerebral Haemorrhage S2StrokeType_missing \\\n", "105 1 0 \n", "720 1 0 \n", "759 0 0 \n", "779 0 0 \n", "898 0 0 \n", "\n", " S2TIAInLastMonth_No S2TIAInLastMonth_No but S2TIAInLastMonth_Yes \\\n", "105 0 0 0 \n", "720 0 0 0 \n", "759 0 0 0 \n", "779 0 0 0 \n", "898 0 0 0 \n", "\n", " S2TIAInLastMonth_missing S2Thrombolysis \n", "105 1 0 \n", "720 1 0 \n", "759 1 0 \n", "779 1 1 \n", "898 1 1 \n", "\n", "[5 rows x 101 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_patients_df.head(5)" ] }, { "cell_type": "markdown", "id": "cb4c81e3", "metadata": {}, "source": [ "## Misclassifications" ] }, { "cell_type": "markdown", "id": "52c377d2", "metadata": {}, "source": [ "If a patient is misclassified by the hospital model for the hospital that they attended, this suggests there is a patient in the data set used to train the model that is similar yet was treated differently" ] }, { "cell_type": "markdown", "id": "8a531ac7", "metadata": {}, "source": [ "### Find misclassified patients in test cohort" ] }, { "cell_type": "code", "execution_count": 6, "id": "0ea924ed", "metadata": {}, "outputs": [], "source": [ "forest, threshold, _, _ = hospital2model[hospital]" ] }, { "cell_type": "code", "execution_count": 7, "id": "26a71ae2", "metadata": {}, "outputs": [], "source": [ "y_test = test_patients_df['S2Thrombolysis'].values\n", "X_test = test_patients_df.drop(['StrokeTeam', 'S2Thrombolysis'], axis=1).values\n", "\n", "y_prob = forest.predict_proba(X_test)[:,1]\n", "y_pred = np.array([1 if p >= threshold else 0 for p in y_prob])" ] }, { "cell_type": "code", "execution_count": 8, "id": "b607510d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 8, 16, 17, 20, 21, 22, 33, 39])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "misclassified = np.where(y_pred!=y_test)[0]\n", "misclassified" ] }, { "cell_type": "markdown", "id": "d029378b", "metadata": {}, "source": [ "### Posterior probability distribution" ] }, { "cell_type": "code", "execution_count": 9, "id": "4426c533", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANzklEQVR4nO3cf6jd9X3H8eerJmVjdjiWO5T88Lo1UHTUKbdR1/2RjQ2MFdKywCJDwQ2Czo4KLVT6h2WMQfdPGRpnCKt0QqmM6STUhFJoh0oX501I0mp0ZMXhxYC3dksalLZx7/1xj8v1eu4933tz7jnJJ88HHDznfD/33Hc+OXl6OL9SVUiSLn4fGvcAkqThMOiS1AiDLkmNMOiS1AiDLkmNWDOuX7xu3bqanJwc16+XpIvSoUOHflxVE/2OjS3ok5OTTE9Pj+vXS9JFKcl/LXbMp1wkqREGXZIaYdAlqREGXZIaYdAlqREGXZIaMTDoSX4pyb8nOZrkpSR/1WdNkjyU5ESSY0luXJ1xJUmL6fI+9J8Bf1BVZ5KsBZ5PcqCqDs5bsw3Y3DvdBDza+68kaUQGPkKvOWd6F9f2Tgu/RH078Hhv7UHgiiRXDXdUSdJSOn1SNMllwCHgo8AjVfXCgiXrgdfnXZ7pXXdywe3sAnYBbNq0aYUjS+2afOCZcY+wYq995VMr+rlL8c+8Wjq9KFpV71bV7wAbgC1JfnvBkvT7sT63s7eqpqpqamKi71cRSJJWaFnvcqmq/wH+Fbh1waEZYOO8yxuAN85nMEnS8nR5l8tEkit6538Z+EPglQXL9gF39d7tcjNwqqpOIkkamS7PoV8F/GPvefQPAf9UVd9Kcg9AVe0B9gO3ASeAt4G7V2leSdIiBga9qo4BN/S5fs+88wXcN9zRJEnL4SdFJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRA4OeZGOS7yU5nuSlJJ/rs2ZrklNJjvROD67OuJKkxazpsOYs8PmqOpzkI8ChJN+pqpcXrHuuqm4f/oiSpC4GPkKvqpNVdbh3/qfAcWD9ag8mSVqeZT2HnmQSuAF4oc/hW5IcTXIgyXWL/PyuJNNJpmdnZ5c/rSRpUZ2DnuRy4Eng/qo6veDwYeDqqroeeBh4ut9tVNXeqpqqqqmJiYkVjixJ6qdT0JOsZS7m36iqpxYer6rTVXWmd34/sDbJuqFOKklaUpd3uQT4GnC8qr66yJore+tIsqV3u28Nc1BJ0tK6vMvlk8CdwA+SHOld9yVgE0BV7QF2APcmOQu8A+ysqhr+uJKkxQwMelU9D2TAmt3A7mENJUlaPj8pKkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1IiBQU+yMcn3khxP8lKSz/VZkyQPJTmR5FiSG1dnXEnSYtZ0WHMW+HxVHU7yEeBQku9U1cvz1mwDNvdONwGP9v4rSRqRgY/Qq+pkVR3unf8pcBxYv2DZduDxmnMQuCLJVUOfVpK0qGU9h55kErgBeGHBofXA6/Muz/DB6JNkV5LpJNOzs7PLHFWStJTOQU9yOfAkcH9VnV54uM+P1AeuqNpbVVNVNTUxMbG8SSVJS+oU9CRrmYv5N6rqqT5LZoCN8y5vAN44//EkSV11eZdLgK8Bx6vqq4ss2wfc1Xu3y83Aqao6OcQ5JUkDdHmXyyeBO4EfJDnSu+5LwCaAqtoD7AduA04AbwN3D31SSdKSBga9qp6n/3Pk89cUcN+whpIkLZ+fFJWkRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRgwMepLHkryZ5IeLHN+a5FSSI73Tg8MfU5I0yJoOa74O7AYeX2LNc1V1+1AmkiStyMBH6FX1LPCTEcwiSToPw3oO/ZYkR5McSHLdYouS7EoynWR6dnZ2SL9akgTDCfph4Oqquh54GHh6sYVVtbeqpqpqamJiYgi/WpL0nvMOelWdrqozvfP7gbVJ1p33ZJKkZTnvoCe5Mkl657f0bvOt871dSdLyDHyXS5JvAluBdUlmgC8DawGqag+wA7g3yVngHWBnVdWqTSxJ6mtg0KvqjgHHdzP3tkZJ0hj5SVFJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGDAx6kseSvJnkh4scT5KHkpxIcizJjcMfU5I0SJdH6F8Hbl3i+DZgc++0C3j0/MeSJC3XwKBX1bPAT5ZYsh14vOYcBK5IctWwBpQkdbNmCLexHnh93uWZ3nUnFy5Msou5R/Fs2rRpxb9w8oFnVvyzF4LXvvKpcY9wUfDvWVqeYbwomj7XVb+FVbW3qqaqampiYmIIv1qS9J5hBH0G2Djv8gbgjSHcriRpGYYR9H3AXb13u9wMnKqqDzzdIklaXQOfQ0/yTWArsC7JDPBlYC1AVe0B9gO3ASeAt4G7V2tYSdLiBga9qu4YcLyA+4Y2kSRpRfykqCQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1olPQk9ya5NUkJ5I80Of41iSnkhzpnR4c/qiSpKWsGbQgyWXAI8AfATPAi0n2VdXLC5Y+V1W3r8KMkqQOujxC3wKcqKofVdXPgSeA7as7liRpuboEfT3w+rzLM73rFrolydEkB5Jc1++GkuxKMp1kenZ2dgXjSpIW0yXo6XNdLbh8GLi6qq4HHgae7ndDVbW3qqaqampiYmJZg0qSltYl6DPAxnmXNwBvzF9QVaer6kzv/H5gbZJ1Q5tSkjRQl6C/CGxOck2SDwM7gX3zFyS5Mkl657f0bvetYQ8rSVrcwHe5VNXZJJ8Fvg1cBjxWVS8luad3fA+wA7g3yVngHWBnVS18WkaStIoGBh3+/2mU/Quu2zPv/G5g93BHkyQth58UlaRGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJakSnoCe5NcmrSU4keaDP8SR5qHf8WJIbhz+qJGkpA4Oe5DLgEWAbcC1wR5JrFyzbBmzunXYBjw55TknSAF0eoW8BTlTVj6rq58ATwPYFa7YDj9ecg8AVSa4a8qySpCWs6bBmPfD6vMszwE0d1qwHTs5flGQXc4/gAc4keRVYB/x4GTNf9PK3Sx6+5PZjCRf1Xgz4e16JC3o/VuHPu5QLYi9G/Gd+z9WLHegS9PS5rlawhqraC+x93w8m01U11WGOS4L7cY578X7uxznuRX9dnnKZATbOu7wBeGMFayRJq6hL0F8ENie5JsmHgZ3AvgVr9gF39d7tcjNwqqpOLrwhSdLqGfiUS1WdTfJZ4NvAZcBjVfVSknt6x/cA+4HbgBPA28Ddy5hh7+AllxT34xz34v3cj3Pciz5S9YGnuiVJFyE/KSpJjTDoktSIkQW9w9cHfCzJvyX5WZIvjGquceiwF3/a+wqFY0m+n+T6ccw5Kh32Y3tvL44kmU7ye+OYcxQG7cW8dZ9I8m6SHaOcb9Q63De2JjnVu28cSfLgOOa8YFTVqp+YezH1P4HfBD4MHAWuXbDmN4BPAH8DfGEUc43j1HEvfhf4td75bcAL4557zPtxOede7/k48Mq45x7XXsxb913m3oywY9xzj/m+sRX41rhnvVBOo3qEPvDrA6rqzap6EfjFiGYaly578f2q+u/exYPMva+/VV3240z1/vUCv0KfD601osvXbAD8JfAk8OYohxuDrvuhnlEFfbGvBrgULXcv/hw4sKoTjVen/UjymSSvAM8Afzai2UZt4F4kWQ98BtgzwrnGpeu/lVuSHE1yIMl1oxntwjSqoHf6aoBLROe9SPL7zAX9i6s60Xh1/dqIf6mqjwGfBv56tYcaky578XfAF6vq3dUfZ+y67Mdh4Oqquh54GHh6tYe6kI0q6H41wDmd9iLJx4F/ALZX1Vsjmm0clnXfqKpngd9Ksm61BxuDLnsxBTyR5DVgB/D3ST49kulGb+B+VNXpqjrTO78fWNvofaOTUQW9y9cHXCoG7kWSTcBTwJ1V9R9jmHGUuuzHR5Okd/5G5l4ga/F/cgP3oqquqarJqpoE/hn4i6p6euSTjkaX+8aV8+4bW5hrWov3jU66fNvieasOXx+Q5EpgGvhV4H+T3M/cK9qnRzHjqHTZC+BB4NeZe/QFcLYa/Wa5jvvxx8x9V9AvgHeAP5n3ImkzOu7FJaPjfuwA7k1ylrn7xs4W7xtd+dF/SWqEnxSVpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEb8H7C+r2CfMstsAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "probs = y_prob[misclassified]\n", "\n", "plt.hist(probs, 5, width=0.07)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2fb002d9", "metadata": {}, "source": [ "## Similarity Metric " ] }, { "cell_type": "markdown", "id": "2a716699", "metadata": {}, "source": [ "When a decision tree is fit to the training data, each internal node in the tree will be split into two further nodes based on the feature that maximises the information gain (or minimises the entropy). Once a split results in two nodes that are pure (only containing samples from a single class) no further information gain is possible: the two nodes are leaves, representing the ends of two paths through the tree. As each patient can take only one path through the decision tree, and all patients start in the same node (the root node), we can use these paths to find the similarity between any pair of patients:\n", "\n", "\n", "$S(i,j) = \\frac{P(i,j)}{\\sqrt{P(i)P(j)}}$.\n", "\n", "\n", "Here, $S(i,j)$ represents the similarity between patient $i$ and patient $j$ as a function of each patient's path, $P$, through the decision tree, where $P(i)$ correspond to the path length of patient $i$, $P(j)$ the path length of $j$ and $P(i,j)$ the length of the shared path of $i$ and $j$. \n", "\n", "Path length is measured using the information gain (change in entropy) at each split. Consider patient $z$, who takes a path through the decision tree which is comprised of a set of $N$ nodes, each passed sequentially and with index $n \\in [1,N]$, where $n=1$ represents the root node and $n=N$ the leaf node. If each node with index $n$ has $X_n$ patients passing through it, the length of the path of patient $z$, $P(z)$, is given by:\n", "\n", "\n", "$P(z)= \\sum_{n=1}^{N-1} X_{n+1} (H_n - H_{n+1} \\frac{X_{n+1}}{X_n})$, \n", "\n", "\n", "where $H_n$ is the entropy at node $n$ and the information gain at each split has been weighted by $X_{n+1}$, the total number of patients that move from node $n$ to node $n+1$.\n", "\n", "From the second equation it is clear that if patients $i$ and $j$ take exactly the same path through the decision tree their path lengths are equal: $P(i) = P(j) = P(i,j)$. Substituting this into the first equation it is clear that if two patients take identical paths the similarity between them is one. Conversely, if patients $i$ and $j$ diverge at the root node ($n=1$), their shared path contains only this node: $P(i,j) = 0$ and hence the similarity between them is equal to zero.\n", "\n", "As a random forest is composed of many decision trees, for each pair of patients we take the distance between them as the average over all trees in the forest. Using this measure of similarity, for each patient in each hospital we found the most similar patients that are treated differently." ] }, { "cell_type": "markdown", "id": "5696b0d3", "metadata": {}, "source": [ "### Functions " ] }, { "cell_type": "markdown", "id": "9c304fc7", "metadata": {}, "source": [ "#### Node weights" ] }, { "cell_type": "code", "execution_count": 10, "id": "b17b9b22", "metadata": {}, "outputs": [], "source": [ "def find_information_gain(X_train, y_train, estimator):\n", " \n", " \"\"\"\n", " Function to find the information gain for each branch in a tree\n", " \n", " Input\n", " \n", " X_train: (numpy array) features used to train the DecisionTree classifier\n", " y_train: (numpy array) target used to train the DecisionTree classifier\n", " estimator: (sklearn model) trained DecisionTree classifier\n", " \n", " Output\n", " \n", " info_gain: (numpy array) information gain associated with move to child \n", " node (node i) from parent node.\n", " \"\"\"\n", " \n", " \n", " # claculate the entropy of each node\n", " \n", " y_true = np.where(y_train==1)\n", " y_false = np.where(y_train==0)\n", " \n", " X_true = X_train[y_true]\n", " X_false = X_train[y_false]\n", " \n", " node_indicator_false = estimator.decision_path(X_false).toarray()\n", " node_indicator_true = estimator.decision_path(X_true).toarray()\n", " \n", " total_passing_true = node_indicator_true.sum(axis=0)\n", " total_passing_false = node_indicator_false.sum(axis=0)\n", " \n", " n_nodes = len(total_passing_true)\n", " \n", " entropies = np.zeros(n_nodes)\n", " \n", " left_child = estimator.tree_.children_left\n", " right_child = estimator.tree_.children_right\n", " \n", " for i in range(n_nodes):\n", " \n", " true = total_passing_true[i]\n", " false = total_passing_false[i]\n", " \n", " if true==0 and false==0:\n", " \n", " print('Node Not Passed')\n", " \n", " continue\n", " \n", " if true==0 or false==0:\n", " \n", " entropies[i] = 0\n", " \n", " else:\n", " \n", " entropies[i] = -(true/(true+false)) * np.log2(true/(true+false)) \\\n", " - (false/(true+false))*np.log2(false/(true+false))\n", " \n", "\n", " \n", " # use the entropy to calculate the information gain for each node\n", " \n", " info_gain = np.zeros(n_nodes)\n", " \n", " \n", " total_passing = estimator.decision_path(X_train).toarray().sum(axis=0)\n", " \n", " for i in range(1,n_nodes):\n", " \n", "\n", " loc_left = np.where(left_child==i)[0]\n", " loc_right = np.where(right_child==i)[0]\n", " \n", " if len(loc_left==1):\n", " \n", " parent = loc_left[0]\n", " \n", " \n", " elif len(loc_right==1):\n", " \n", " parent = loc_right[0]\n", " \n", " else:\n", " \n", " raise Exception(\"Cant find parent\")\n", " \n", " \n", " info_gain[i] = entropies[parent] - \\\n", " total_passing[i]*entropies[i]/total_passing[parent]\n", "\n", " \n", " \n", " return info_gain" ] }, { "cell_type": "code", "execution_count": 11, "id": "17787553", "metadata": {}, "outputs": [], "source": [ "def find_node_weight(X_train, y_train, estimator):\n", " \n", " \"\"\"\n", " Function to find the weight of a node. Weight is defined as the product\n", " of the number of data points passing the node (flow) and the information\n", " gain at the node.\n", " \n", " Input\n", " \n", " X_train: (numpy array) features used to train the DecisionTree classifier\n", " y_train: (numpy array) target used to train the DecisionTree classifier\n", " estimator: (sklearn model) trained DecisionTree classifier\n", " \n", " Output\n", " \n", " weight: (numpy array) weight (flows * info_gain) of each node\n", " \n", " \"\"\"\n", " \n", " node_indicator = estimator.decision_path(X_train).toarray()\n", " \n", " flows = node_indicator.sum(axis=0)\n", "\n", " info_gain = find_information_gain(X_train, y_train, estimator)\n", " \n", " if len(flows) != len(info_gain):\n", " \n", " raise Exception(\"Length mismatch\")\n", " \n", " return flows*info_gain" ] }, { "cell_type": "code", "execution_count": 12, "id": "f3d67081", "metadata": {}, "outputs": [], "source": [ "def find_forest_weight(X_train, y_train, forest):\n", " \n", " trees = forest.estimators_\n", " \n", " results = []\n", " \n", " for i,estimator in enumerate(trees):\n", " \n", " weight = find_node_weight(X_train, y_train, estimator)\n", " \n", " results.append(weight)\n", " \n", " return np.array(results)" ] }, { "cell_type": "markdown", "id": "663f9cef", "metadata": {}, "source": [ "#### Caclulate Similarity " ] }, { "cell_type": "code", "execution_count": 13, "id": "f9a56351", "metadata": {}, "outputs": [], "source": [ "def find_similar_patients_tree(patient, test_set, estimator, weights):\n", " \n", " \"\"\"\n", " Function to calculate the similarity between a single patient and all other\n", " patients in a data set using a single DecisionTree classifier\n", " \n", " Input\n", " \n", " patient: index of comparison patient in test_set\n", " test_set: sub-set of n data points unseen by estimator. Must include \n", " patient\n", " estimator: trained sklearn DecisionTreeClassifier\n", " weights: array of length n-nodes. \n", " \n", " Output\n", " \n", " similarity: (n-dim array) index of patients and similarity\n", " \n", " \"\"\"\n", " \n", " similarity = np.zeros(test_set.shape[0])\n", " \n", " paths = estimator.decision_path(test_set)\n", " \n", " patient_nodes = paths.toarray()[patient]\n", " \n", " for n in range(test_set.shape[0]):\n", " \n", " sample_ids = [patient, n]\n", " \n", " patient_paths = paths.toarray()[sample_ids]\n", " \n", " common_nodes = (patient_paths.sum(axis=0) ==\n", " len(sample_ids))\n", " \n", " common_node_weights = weights[common_nodes]\n", " \n", " path_length = sum(common_node_weights)\n", " \n", " p0_path_length = sum(patient_paths[0]*weights)\n", " p1_path_length = sum(patient_paths[1]*weights)\n", " \n", " similar = path_length / np.sqrt(p0_path_length*p1_path_length)\n", " \n", " similarity[n] = similar\n", " \n", " return similarity" ] }, { "cell_type": "code", "execution_count": 14, "id": "6492ca30", "metadata": {}, "outputs": [], "source": [ "def find_similar_patients(patient, test_set, forest, weights):\n", " \n", " \"\"\"\n", " Function to calculate the similarity between a single patient and all other\n", " patients in a data set using a RandomForest classifier\n", " \n", " Input\n", " \n", " patient: index of comparison patient in test_set\n", " test_set: sub-set of n data points unseen by estimator. Must include \n", " patient\n", " forest: trained sklearn RandomForestClassifier\n", " weights: array of length n-nodes. \n", " \n", " Output\n", " \n", " results: (n-dim array) similarity between patient and test_set for each tree\n", " in the RandomForest classifier \n", " \"\"\"\n", " \n", " trees = forest.estimators_\n", " \n", " results = np.zeros((len(trees), test_set.shape[0]))\n", " \n", " for i,estimator in enumerate(trees):\n", " \n", " similarity = find_similar_patients_tree(patient, test_set, estimator, weights[i])\n", " \n", " results[i] = similarity\n", " \n", " return results" ] }, { "cell_type": "markdown", "id": "96f7229e", "metadata": {}, "source": [ "### Example hospital " ] }, { "cell_type": "code", "execution_count": 15, "id": "f314c40a", "metadata": {}, "outputs": [], "source": [ "forest, threshold, train_patients, train_outcomes = hospital2model[hospital]" ] }, { "cell_type": "code", "execution_count": 16, "id": "47ccace8", "metadata": {}, "outputs": [], "source": [ "weights = find_forest_weight(train_patients, train_outcomes, forest)" ] }, { "cell_type": "code", "execution_count": 17, "id": "7ad40b9a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100,)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights.shape" ] }, { "cell_type": "markdown", "id": "0faaa68d", "metadata": {}, "source": [ "### Pairwise similarity " ] }, { "cell_type": "code", "execution_count": 18, "id": "13ba14c2", "metadata": {}, "outputs": [], "source": [ "test_patients = test_patients_df.drop(['StrokeTeam', 'S2Thrombolysis'], axis=1).values\n", "test_outcomes = test_patients_df['S2Thrombolysis'].values" ] }, { "cell_type": "code", "execution_count": 19, "id": "5e25ef1f", "metadata": {}, "outputs": [], "source": [ "similarity = find_similar_patients(0, test_patients, forest, weights)" ] }, { "cell_type": "code", "execution_count": 20, "id": "e3acbaf6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 42)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "similarity.shape" ] }, { "cell_type": "markdown", "id": "a4bf3081", "metadata": {}, "source": [ "`similarity` contains one row for each decision tree in the random forest classifier. Each row contains the pairwise similarity between `patient` and all other patients. \n", "\n", "Note that the similarity between `patient` and itself (in this case, element `0` of each row) is always equal to 1." ] }, { "cell_type": "code", "execution_count": 21, "id": "ada9719c", "metadata": {}, "outputs": [], "source": [ "# average similarity across all trees\n", "results = similarity.sum(axis=0)/100" ] }, { "cell_type": "code", "execution_count": 22, "id": "8eb88182", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1. , 0.41878349, 0.41385524, 0.30315102, 0.28188906,\n", " 0.31645625, 0.44306756, 0.17333223, 0.35602158, 0.46386653,\n", " 0.2490115 , 0.33010815, 0.50437351, 0.12329451, 0.17538473,\n", " 0.19784455, 0.35274581, 0.29956862, 0.29864365, 0.2882998 ,\n", " 0.18215119, 0.39094488, 0.35923549, 0.39327842, 0.41762123,\n", " 0.48577199, 0.11948557, 0.21997094, 0.40880923, 0.50791922,\n", " 0.38785235, 0.30787927, 0.53550742, 0.42006776, 0.23841425,\n", " 0.3628097 , 0.20345056, 0.33015945, 0.16756539, 0.24487966,\n", " 0.16356513, 0.29071159])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results" ] }, { "cell_type": "markdown", "id": "18c51af6", "metadata": {}, "source": [ "## Most similar patients with a different outcome " ] }, { "cell_type": "markdown", "id": "ad6ccf03", "metadata": {}, "source": [ "### Identify patients with a predicted outcome different to their true outcome" ] }, { "cell_type": "code", "execution_count": 23, "id": "c48eafbb", "metadata": {}, "outputs": [], "source": [ "predicted_outcome = forest.predict(test_patients)\n", "\n", "patient_index = np.where(predicted_outcome!=test_outcomes)[0]" ] }, { "cell_type": "code", "execution_count": 24, "id": "4268bca3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 8, 17, 20, 29, 39])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patient_index" ] }, { "cell_type": "markdown", "id": "b66b5c40", "metadata": {}, "source": [ "Show actual outcomes for patients:" ] }, { "cell_type": "code", "execution_count": 25, "id": "d98feee8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Actual: [0, 1, 1, 1, 1]\n" ] } ], "source": [ "actual = [test_outcomes[i] for i in patient_index]\n", "\n", "print ('Actual:', actual)" ] }, { "cell_type": "markdown", "id": "a478ae9f", "metadata": {}, "source": [ "### For each patient, find 5 most similar patients with a different outcome in training set " ] }, { "cell_type": "code", "execution_count": 26, "id": "8202ce5c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.32 s, sys: 0 ns, total: 7.32 s\n", "Wall time: 7.32 s\n" ] } ], "source": [ "%%time\n", "\n", "most_similar = []\n", "\n", "for i in patient_index:\n", " \n", " patient = test_patients[i]\n", " \n", " outcome = test_outcomes[i]\n", " \n", " opposite = np.where(train_outcomes != outcome)[0]\n", " \n", "\n", " X = np.vstack((patient, train_patients[opposite]))\n", " y = np.concatenate(([outcome], train_outcomes[opposite]))\n", "\n", " results = find_similar_patients(0, X, forest, weights)\n", " \n", " summed_results = results.sum(axis=0)[1:] #remove similarity between patient and itself\n", " \n", " five_indices = np.argpartition(summed_results, -5)[-5:]\n", "\n", " similar_patients = train_patients[opposite][five_indices]\n", "\n", " S = summed_results[five_indices]\n", "\n", " most_similar.append([S, summed_results, outcome, i, patient, similar_patients])\n", " " ] }, { "cell_type": "markdown", "id": "2d1b54a8", "metadata": {}, "source": [ "### Get feature importances for hospital " ] }, { "cell_type": "code", "execution_count": 27, "id": "829aeb19", "metadata": {}, "outputs": [], "source": [ "features = cohort.drop(['StrokeTeam', 'S2Thrombolysis'], axis=1).columns.values\n", "importances = forest.feature_importances_" ] }, { "cell_type": "markdown", "id": "118489c3", "metadata": {}, "source": [ "## Example where thrombolysis was not given when expected" ] }, { "cell_type": "code", "execution_count": 28, "id": "05c15010", "metadata": {}, "outputs": [], "source": [ "patient_of_interest = 0" ] }, { "cell_type": "markdown", "id": "9b3a67b6", "metadata": {}, "source": [ "### Create dataframe for patient" ] }, { "cell_type": "code", "execution_count": 29, "id": "483c51ed", "metadata": {}, "outputs": [], "source": [ "S, _, _, _, patient, similar_patients = most_similar[patient_of_interest]" ] }, { "cell_type": "code", "execution_count": 30, "id": "211db419", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 3, 4, 2])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S_index = np.argsort(S)\n", "S_index" ] }, { "cell_type": "code", "execution_count": 31, "id": "ef8779ef", "metadata": {}, "outputs": [], "source": [ "patient_df = pd.DataFrame(index = features)\n", "patient_df['Importance'] = importances\n", "patient_df['Patient'] = patient\n", "\n", "for i,s in enumerate(S_index):\n", " \n", " patient_df[str(i+1)] = similar_patients[s]" ] }, { "cell_type": "markdown", "id": "1e08e6e4", "metadata": {}, "source": [ "### Sort dataframe according to feature importance " ] }, { "cell_type": "code", "execution_count": 32, "id": "c0912e6a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Patient12345
S2BrainImagingTime_min23.019.011.033.030.012.0
S2NihssArrival4.05.06.09.06.08.0
S1OnsetToArrival_min83.0149.0134.0121.079.082.0
S2StrokeType_Primary Intracerebral Haemorrhage0.00.00.00.00.00.0
S2RankinBeforeStroke0.00.00.00.01.00.0
.....................
AFAnticoagulentHeparin_Yes0.00.00.00.00.00.0
S1OnsetInHospital_Yes0.00.00.00.00.00.0
S1OnsetInHospital_No1.01.01.01.01.01.0
S1Ethnicity_Mixed0.00.00.00.00.00.0
S1OnsetTimeType_Not known0.00.00.00.00.00.0
\n", "

99 rows × 6 columns

\n", "
" ], "text/plain": [ " Patient 1 2 3 \\\n", "S2BrainImagingTime_min 23.0 19.0 11.0 33.0 \n", "S2NihssArrival 4.0 5.0 6.0 9.0 \n", "S1OnsetToArrival_min 83.0 149.0 134.0 121.0 \n", "S2StrokeType_Primary Intracerebral Haemorrhage 0.0 0.0 0.0 0.0 \n", "S2RankinBeforeStroke 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... \n", "AFAnticoagulentHeparin_Yes 0.0 0.0 0.0 0.0 \n", "S1OnsetInHospital_Yes 0.0 0.0 0.0 0.0 \n", "S1OnsetInHospital_No 1.0 1.0 1.0 1.0 \n", "S1Ethnicity_Mixed 0.0 0.0 0.0 0.0 \n", "S1OnsetTimeType_Not known 0.0 0.0 0.0 0.0 \n", "\n", " 4 5 \n", "S2BrainImagingTime_min 30.0 12.0 \n", "S2NihssArrival 6.0 8.0 \n", "S1OnsetToArrival_min 79.0 82.0 \n", "S2StrokeType_Primary Intracerebral Haemorrhage 0.0 0.0 \n", "S2RankinBeforeStroke 1.0 0.0 \n", "... ... ... \n", "AFAnticoagulentHeparin_Yes 0.0 0.0 \n", "S1OnsetInHospital_Yes 0.0 0.0 \n", "S1OnsetInHospital_No 1.0 1.0 \n", "S1Ethnicity_Mixed 0.0 0.0 \n", "S1OnsetTimeType_Not known 0.0 0.0 \n", "\n", "[99 rows x 6 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patient_df = patient_df.sort_values(by='Importance', ascending=False)\n", "patient_df.drop(['Importance'], axis=1, inplace=True)\n", "patient_df.to_csv('./output/similar_patients_treated_differently_1.csv')\n", "\n", "patient_df" ] }, { "cell_type": "markdown", "id": "af5231e6", "metadata": {}, "source": [ "### Rescale " ] }, { "cell_type": "markdown", "id": "2ff60b2c", "metadata": {}, "source": [ "As some features, such as stroke to arrival time and age, have a much larger range than other features, we rescale them so that all features can be visualised on one axis" ] }, { "cell_type": "code", "execution_count": 33, "id": "63089ac4", "metadata": {}, "outputs": [], "source": [ "def rescale(row, df, time=False, age=False):\n", " \n", " vals = df.iloc[row].values\n", " \n", " if len(set(vals))==1:\n", " \n", " rescaled=vals\n", " \n", " if time==True:\n", " \n", " rescaled=vals/60\n", " \n", " elif age==True:\n", " \n", " rescaled=vals/10\n", " \n", " else:\n", " #rescaled = [(v - vals[0]) for v in vals]\n", " rescaled = vals\n", " \n", " return rescaled" ] }, { "cell_type": "code", "execution_count": 34, "id": "7bb89dd4", "metadata": {}, "outputs": [], "source": [ "rescaled_results = patient_df.copy()\n", "\n", "indx_list = rescaled_results.index.tolist()\n", "\n", "for i in range(len(rescaled_results)):\n", " \n", " indx = rescaled_results.index[i]\n", " \n", " if indx in ['S1OnsetToArrival_min']:#, 'S2BrainImagingTime_min']:\n", " \n", " rescaled = rescale(i,rescaled_results, time=True)\n", " indx_list[i] = indx.split('_')[0] + '_hours'\n", " \n", " elif indx in ['S1AgeOnArrival']:\n", " \n", " rescaled = rescale(i,rescaled_results, age=True)\n", " indx_list[i] = indx + '_decades'\n", " \n", " else:\n", " rescaled = rescale(i,rescaled_results)\n", " \n", " rescaled_results.loc[indx]=rescaled\n", " \n", "rescaled_results.index = indx_list" ] }, { "cell_type": "markdown", "id": "543f73b9", "metadata": {}, "source": [ "### Plot " ] }, { "cell_type": "code", "execution_count": 35, "id": "4a4cee5c", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rescaled_results.plot(kind='bar', stacked=False, alpha=1, figsize=(8,8),\n", " xlabel='Feature', ylabel='Feature value') \n", "\n", "plt.xlim(None,10.5)\n", "\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figures/within_hospital_variability/5_similar_bar_patient_not_treated.jpg', dpi=300)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "id": "6450df6c", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize=(8,8))\n", "\n", "xvals = rescaled_results.index.values\n", "pvals = rescaled_results['Patient']\n", "mvals = [np.mean(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "lvals = [min(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "hvals = [max(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "\n", "plt.plot(xvals, mvals, 'r*', ms=10, label='Mean')\n", "plt.plot(xvals, lvals, 'b*', ms=10, label='Low')\n", "plt.plot(xvals, hvals, 'y*', ms=10, label='High')\n", "plt.plot(xvals, pvals, 'k*', ms=10, label='Patient')\n", "plt.fill_between(xvals, lvals, hvals, color='g', alpha=0.5)\n", "plt.xlim(-0.5,10.5)\n", "plt.xticks(rotation=90)\n", "plt.legend(loc='best')\n", "ax.set_xlabel('Feature')\n", "ax.set_ylabel('Feature value')\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figures/within_hospital_variability/5_similar_range_patient_not_treated.jpg', dpi=300)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1ebede3a", "metadata": {}, "source": [ "## Example where thrombolysis was given when not expected" ] }, { "cell_type": "code", "execution_count": 37, "id": "1d9497fd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patient_of_interest = 1\n", "\n", "S, _, _, _, patient, similar_patients = most_similar[patient_of_interest]\n", "S_index = np.argsort(S)\n", "S_index" ] }, { "cell_type": "code", "execution_count": 38, "id": "c614b30c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Patient12345
S2BrainImagingTime_min40.088.040.027.051.035.0
S2NihssArrival9.011.010.06.06.06.0
S1OnsetToArrival_min105.067.0122.083.075.0160.0
S2StrokeType_Primary Intracerebral Haemorrhage0.00.00.00.00.00.0
S2RankinBeforeStroke0.00.04.00.03.00.0
.....................
AFAnticoagulentHeparin_Yes0.00.00.00.00.00.0
S1OnsetInHospital_Yes0.00.00.00.00.00.0
S1OnsetInHospital_No1.01.01.01.01.01.0
S1Ethnicity_Mixed0.00.00.00.00.00.0
S1OnsetTimeType_Not known0.00.00.00.00.00.0
\n", "

99 rows × 6 columns

\n", "
" ], "text/plain": [ " Patient 1 2 3 \\\n", "S2BrainImagingTime_min 40.0 88.0 40.0 27.0 \n", "S2NihssArrival 9.0 11.0 10.0 6.0 \n", "S1OnsetToArrival_min 105.0 67.0 122.0 83.0 \n", "S2StrokeType_Primary Intracerebral Haemorrhage 0.0 0.0 0.0 0.0 \n", "S2RankinBeforeStroke 0.0 0.0 4.0 0.0 \n", "... ... ... ... ... \n", "AFAnticoagulentHeparin_Yes 0.0 0.0 0.0 0.0 \n", "S1OnsetInHospital_Yes 0.0 0.0 0.0 0.0 \n", "S1OnsetInHospital_No 1.0 1.0 1.0 1.0 \n", "S1Ethnicity_Mixed 0.0 0.0 0.0 0.0 \n", "S1OnsetTimeType_Not known 0.0 0.0 0.0 0.0 \n", "\n", " 4 5 \n", "S2BrainImagingTime_min 51.0 35.0 \n", "S2NihssArrival 6.0 6.0 \n", "S1OnsetToArrival_min 75.0 160.0 \n", "S2StrokeType_Primary Intracerebral Haemorrhage 0.0 0.0 \n", "S2RankinBeforeStroke 3.0 0.0 \n", "... ... ... \n", "AFAnticoagulentHeparin_Yes 0.0 0.0 \n", "S1OnsetInHospital_Yes 0.0 0.0 \n", "S1OnsetInHospital_No 1.0 1.0 \n", "S1Ethnicity_Mixed 0.0 0.0 \n", "S1OnsetTimeType_Not known 0.0 0.0 \n", "\n", "[99 rows x 6 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patient_df = pd.DataFrame(index = features)\n", "patient_df['Importance'] = importances\n", "patient_df['Patient'] = patient\n", "\n", "for i,s in enumerate(S_index):\n", " patient_df[str(i+1)] = similar_patients[s]\n", " \n", "patient_df = patient_df.sort_values(by='Importance', ascending=False)\n", "patient_df.drop(['Importance'], axis=1, inplace=True)\n", "patient_df.to_csv('./output/similar_patients_treated_differently_2.csv')\n", "\n", "patient_df" ] }, { "cell_type": "markdown", "id": "00baf916", "metadata": {}, "source": [ "Rescale:" ] }, { "cell_type": "code", "execution_count": 39, "id": "5d6dc308", "metadata": {}, "outputs": [], "source": [ "rescaled_results = patient_df.copy()\n", "\n", "indx_list = rescaled_results.index.tolist()\n", "\n", "for i in range(len(rescaled_results)):\n", " \n", " indx = rescaled_results.index[i]\n", " \n", " if indx in ['S1OnsetToArrival_min']:#, 'S2BrainImagingTime_min']:\n", " \n", " rescaled = rescale(i,rescaled_results, time=True)\n", " indx_list[i] = indx.split('_')[0] + '_hours'\n", " \n", " elif indx in ['S1AgeOnArrival']:\n", " \n", " rescaled = rescale(i,rescaled_results, age=True)\n", " indx_list[i] = indx + '_decades'\n", " \n", " else:\n", " rescaled = rescale(i,rescaled_results)\n", " \n", " rescaled_results.loc[indx]=rescaled\n", " \n", "rescaled_results.index = indx_list" ] }, { "cell_type": "markdown", "id": "34cbca51", "metadata": {}, "source": [ "Plot" ] }, { "cell_type": "code", "execution_count": 40, "id": "b5c894c0", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rescaled_results.plot(kind='bar', stacked=False, alpha=1, figsize=(8,8),\n", " xlabel='Feature', ylabel='Feature value')\n", "\n", "plt.xlim(None,10.5)\n", "\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figures/within_hospital_variability/5_similar_bar_patient_treated.jpg', dpi=300)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "id": "7b76e90d", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize=(8,8))\n", "\n", "xvals = rescaled_results.index.values\n", "pvals = rescaled_results['Patient']\n", "mvals = [np.mean(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "lvals = [min(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "hvals = [max(rescaled_results.loc[feature].values[1:]) for feature in rescaled_results.index]\n", "\n", "plt.plot(xvals, mvals, 'r*', ms=10, label='Mean')\n", "plt.plot(xvals, lvals, 'b*', ms=10, label='Low')\n", "plt.plot(xvals, hvals, 'y*', ms=10, label='High')\n", "plt.plot(xvals, pvals, 'k*', ms=10, label='Patient')\n", "plt.fill_between(xvals, lvals, hvals, color='g', alpha=0.5)\n", "plt.xlim(-0.5,10.5)\n", "plt.xticks(rotation=90)\n", "plt.legend(loc='best')\n", "ax.set_xlabel('Feature')\n", "ax.set_ylabel('Feature value')\n", "\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figures/within_hospital_variability/5_similar_range_patient_treated.jpg', dpi=300)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "355b7c28", "metadata": {}, "source": [ "## Observations\n", "\n", "- Misclassifications indicate that there is variability in clinical decision making within a hospital\n", "- We can use the structure of the Random Forest classifier to define a similarity metric\n", "- For patients that are incorrectly classified, we can find similar patients that were treated differently, further demonstrating variation in decision making within a hospital" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 5 }