{ "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": [ "
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agegendernum_prior_positiveracescanner_typeground_truthalgorithm_output
053F0hispanichologicnormalnormal
139F0caucasianhologicnormalabnormal
256F0african_americanhologicnormalnormal
344F0caucasianhologicnormalabnormal
435F0caucasianhologicnormalnormal
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" ], "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": [ "
" ] }, "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": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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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\n", "text/plain": [ "
" ] }, "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": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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 }