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--- a +++ b/1. Applying AI to 2D Medical Imaging Data/12. Intended Use and Clinical Impact Exercise/solution.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sklearn.metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>age</th>\n", + " <th>gender</th>\n", + " <th>num_prior_positive</th>\n", + " <th>race</th>\n", + " <th>scanner_type</th>\n", + " <th>ground_truth</th>\n", + " <th>algorithm_output</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>53</td>\n", + " <td>F</td>\n", + " <td>0</td>\n", + " <td>hispanic</td>\n", + " <td>hologic</td>\n", + " <td>normal</td>\n", + " <td>normal</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>39</td>\n", + " <td>F</td>\n", + " <td>0</td>\n", + " <td>caucasian</td>\n", + " <td>hologic</td>\n", + " <td>normal</td>\n", + " <td>abnormal</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>56</td>\n", + " <td>F</td>\n", + " <td>0</td>\n", + " <td>african_american</td>\n", + " <td>hologic</td>\n", + " <td>normal</td>\n", + " <td>normal</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>44</td>\n", + " <td>F</td>\n", + " <td>0</td>\n", + " <td>caucasian</td>\n", + " <td>hologic</td>\n", + " <td>normal</td>\n", + " <td>abnormal</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>35</td>\n", + " <td>F</td>\n", + " <td>0</td>\n", + " <td>caucasian</td>\n", + " <td>hologic</td>\n", + " <td>normal</td>\n", + " <td>normal</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " age gender num_prior_positive race scanner_type ground_truth \\\n", + "0 53 F 0 hispanic hologic normal \n", + "1 39 F 0 caucasian hologic normal \n", + "2 56 F 0 african_american hologic normal \n", + "3 44 F 0 caucasian hologic normal \n", + "4 35 F 0 caucasian hologic normal \n", + "\n", + " algorithm_output \n", + "0 normal \n", + "1 abnormal \n", + "2 normal \n", + "3 abnormal \n", + "4 normal " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demos = pd.read_csv('demographics.csv')\n", + "demos.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at the distributions of age, gender, num_prior_positives, race, and scanner_type:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.hist(demos.age)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x7efcf5fa5ed0>" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demos.gender.value_counts().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.hist(demos.num_prior_positive)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x7efcf5f9e9d0>" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demos.race.value_counts().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x7efcf5dffdd0>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demos.scanner_type.value_counts().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statement on intended use/population: \n", + "\n", + "Looking at the above, we see that the algorithm was trained on female patients only who spanned from ages 35 to 61. None of these patients had ever had a prior abnormal mammography study. All patients were scanned on a Hologic scanner, and were either Caucasian, Hispanic, or African American. \n", + "\n", + "From this information, the appropriate intended use statement would be: \n", + "\n", + "This algorithm is intended for use on Caucasian, Hispanic, and African American women from the ages of 35-61 who have been administered a screening mammography study on a Hologic mammography machine and have never before demonstrated an abnormal mammography study. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}