Downloads: 1
445 lines (445 with data), 22.1 kB
{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python3610jvsc74a57bd01452ce364e145c8938a76e90050576b8a2a4d70ee75de50f3361ff243fa2a5f7",
"display_name": "Python 3.6.10 64-bit ('EHRKit': conda)"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Mimic coding \n",
"This notebook walks the user through the training and testing of a icd9 coding classifier, specifically built on and for the MIMIC-III dataset. The notebook utilizes the `mimic_icd9_coding` module, and the example uses a neural network classifier, although any other classifier may be used.\n",
"\n",
"The user must set the root directory for the EHRKit, and optionally the data directories."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"ROOT_EHR_DIR = '<EHRKit Path>' # set your root EHRKit directory here (with the '/' at the end)\n",
"import sys\n",
"import os\n",
"sys.path.append(os.path.dirname(ROOT_EHR_DIR))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Set your mimic path here\n",
"# Put all of the individual mimic csv files in MIMIC_PATH, with the `/` at the end. These files should be all cap csv files, such as NOTEEVENTS.csv. Keep OUTPUT_DATA_PATH empty, the processed data will be deposited there.\n",
"OUTPUT_DATA_PATH = ROOT_EHR_DIR + 'data/output_data/'\n",
"MIMIC_PATH = ROOT_EHR_DIR + 'data/mimic_data/'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
"In /home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
"The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
]
}
],
"source": [
"from mimic_icd9_coding.coding_pipeline import codingPipeline\n",
"from mimic_icd9_coding.utils.mimic_data_preparation import run_mimic_prep"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Run the mimic label preparation, examine the top n most common labels, and create a dataset of labels and text\n",
"run_mimic_prep(output_folder = OUTPUT_DATA_PATH, mimic_data_path= MIMIC_PATH)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Building basic tfidf pipeline\n",
"Iteration 1, loss = 39.26339982\n",
"Iteration 2, loss = 27.15691598\n",
"Iteration 3, loss = 25.43022223\n",
"Iteration 4, loss = 24.39200925\n",
"Iteration 5, loss = 23.53421875\n",
"Iteration 6, loss = 22.71761089\n",
"Iteration 7, loss = 21.98377343\n",
"Iteration 8, loss = 21.40524055\n",
"Iteration 9, loss = 20.91691075\n",
"Iteration 10, loss = 20.46976534\n",
"Iteration 11, loss = 20.05602722\n",
"Iteration 12, loss = 19.67969932\n",
"Iteration 13, loss = 19.32472493\n",
"Iteration 14, loss = 18.98996470\n",
"Iteration 15, loss = 18.69047703\n",
"Iteration 16, loss = 18.41708954\n",
"Iteration 17, loss = 18.16937539\n",
"Iteration 18, loss = 17.94176193\n",
"Iteration 19, loss = 17.72638875\n",
"Iteration 20, loss = 17.52546337\n",
"Iteration 21, loss = 17.33147929\n",
"Iteration 22, loss = 17.14397179\n",
"Iteration 23, loss = 16.97036583\n",
"Iteration 24, loss = 16.80744903\n",
"Iteration 25, loss = 16.65087996\n",
"Iteration 26, loss = 16.50350788\n",
"Iteration 27, loss = 16.36437565\n",
"Iteration 28, loss = 16.22898538\n",
"Iteration 29, loss = 16.10017515\n",
"Iteration 30, loss = 15.97871846\n",
"Iteration 31, loss = 15.86154579\n",
"Iteration 32, loss = 15.75070126\n",
"Iteration 33, loss = 15.64012650\n",
"Iteration 34, loss = 15.53565693\n",
"Iteration 35, loss = 15.43139960\n",
"Iteration 36, loss = 15.32890083\n",
"Iteration 37, loss = 15.23351048\n",
"Iteration 38, loss = 15.14100144\n",
"Iteration 39, loss = 15.05027343\n",
"Iteration 40, loss = 14.96409722\n",
"Iteration 41, loss = 14.88040182\n",
"Iteration 42, loss = 14.80062628\n",
"Iteration 43, loss = 14.72397634\n",
"Iteration 44, loss = 14.64785816\n",
"Iteration 45, loss = 14.57387839\n",
"Iteration 46, loss = 14.50342585\n",
"Iteration 47, loss = 14.43510663\n",
"Iteration 48, loss = 14.36782499\n",
"Iteration 49, loss = 14.30226749\n",
"Iteration 50, loss = 14.24259876\n",
"Iteration 51, loss = 14.17783637\n",
"Iteration 52, loss = 14.12287286\n",
"Iteration 53, loss = 14.05997085\n",
"Iteration 54, loss = 14.00637036\n",
"Iteration 55, loss = 13.94991378\n",
"Iteration 56, loss = 13.89237458\n",
"Iteration 57, loss = 13.83448240\n",
"Iteration 58, loss = 13.77879710\n",
"Iteration 59, loss = 13.72299570\n",
"Iteration 60, loss = 13.66682543\n",
"Iteration 61, loss = 13.61414798\n",
"Iteration 62, loss = 13.56323821\n",
"Iteration 63, loss = 13.51129347\n",
"Iteration 64, loss = 13.46183629\n",
"Iteration 65, loss = 13.41255142\n",
"Iteration 66, loss = 13.36630654\n",
"Iteration 67, loss = 13.31819055\n",
"Iteration 68, loss = 13.27420876\n",
"Iteration 69, loss = 13.23153613\n",
"Iteration 70, loss = 13.18662444\n",
"Iteration 71, loss = 13.14404019\n",
"Iteration 72, loss = 13.10327418\n",
"Iteration 73, loss = 13.06489162\n",
"Iteration 74, loss = 13.02366019\n",
"Iteration 75, loss = 12.98480324\n",
"Iteration 76, loss = 12.94750608\n",
"Iteration 77, loss = 12.90973475\n",
"Iteration 78, loss = 12.86992567\n",
"Iteration 79, loss = 12.83378217\n",
"Iteration 80, loss = 12.80129023\n",
"Iteration 81, loss = 12.76208668\n",
"Iteration 82, loss = 12.72865387\n",
"Iteration 83, loss = 12.69202347\n",
"Iteration 84, loss = 12.65801739\n",
"Iteration 85, loss = 12.62539280\n",
"Iteration 86, loss = 12.59177266\n",
"Iteration 87, loss = 12.56355448\n",
"Iteration 88, loss = 12.52974378\n",
"Iteration 89, loss = 12.49803458\n",
"Iteration 90, loss = 12.47011190\n",
"Iteration 91, loss = 12.44098206\n",
"Iteration 92, loss = 12.41264979\n",
"Iteration 93, loss = 12.38165628\n",
"Iteration 94, loss = 12.35401116\n",
"Iteration 95, loss = 12.32752414\n",
"Iteration 96, loss = 12.29908133\n",
"Iteration 97, loss = 12.27293193\n",
"Iteration 98, loss = 12.24794822\n",
"Iteration 99, loss = 12.22328992\n",
"Iteration 100, loss = 12.19652259\n",
"/home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/sklearn/neural_network/_multilayer_perceptron.py:585: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
" % self.max_iter, ConvergenceWarning)\n",
"/home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"\n",
"Classification Report\n",
"======================================================\n",
"\n",
" precision recall f1-score support\n",
"\n",
" 0 0.66 0.45 0.54 485\n",
" 1 0.40 0.19 0.26 1046\n",
" 2 0.69 0.60 0.64 1826\n",
" 3 0.49 0.25 0.33 2191\n",
" 4 0.83 0.65 0.73 608\n",
" 5 0.54 0.32 0.40 1470\n",
" 6 0.55 0.37 0.44 1327\n",
" 7 0.56 0.25 0.34 337\n",
" 8 0.75 0.55 0.63 748\n",
" 9 0.81 0.75 0.78 385\n",
" 10 0.43 0.16 0.23 486\n",
" 11 0.67 0.56 0.61 337\n",
" 12 0.66 0.49 0.56 538\n",
" 13 0.73 0.59 0.65 542\n",
" 14 0.80 0.70 0.75 1571\n",
" 15 0.88 0.77 0.82 4184\n",
" 16 0.24 0.05 0.08 397\n",
" 17 0.73 0.64 0.68 4229\n",
" 18 0.70 0.51 0.59 679\n",
" 19 0.27 0.02 0.04 354\n",
" 20 0.60 0.53 0.56 4370\n",
" 21 0.65 0.37 0.47 786\n",
" 22 0.40 0.14 0.21 654\n",
" 23 0.50 0.29 0.37 269\n",
" 24 0.58 0.42 0.49 3737\n",
" 25 0.50 0.21 0.29 591\n",
" 26 0.59 0.31 0.41 1175\n",
" 27 0.48 0.11 0.18 485\n",
" 28 0.70 0.50 0.58 308\n",
" 29 0.40 0.06 0.11 458\n",
" 30 0.65 0.44 0.52 560\n",
" 31 0.73 0.44 0.55 416\n",
" 32 0.51 0.30 0.38 846\n",
" 33 0.62 0.39 0.48 603\n",
" 34 0.50 0.35 0.41 1432\n",
" 35 0.71 0.48 0.57 675\n",
" 36 0.50 0.35 0.41 386\n",
" 37 0.42 0.13 0.20 342\n",
" 38 0.64 0.41 0.50 460\n",
" 39 0.57 0.34 0.43 955\n",
" 40 0.52 0.31 0.39 537\n",
" 41 0.59 0.29 0.39 399\n",
" 42 0.71 0.70 0.71 6165\n",
" 43 0.69 0.54 0.61 1873\n",
" 44 0.79 0.69 0.74 1606\n",
" 45 0.43 0.22 0.29 547\n",
" 46 0.34 0.06 0.10 333\n",
" 47 0.83 0.74 0.78 4202\n",
" 48 0.72 0.55 0.62 319\n",
" 49 0.47 0.23 0.31 769\n",
" 50 0.61 0.37 0.46 1620\n",
" 51 0.62 0.37 0.46 631\n",
" 52 0.64 0.30 0.41 431\n",
" 53 0.85 0.76 0.80 5036\n",
" 54 0.76 0.70 0.73 3901\n",
" 55 0.66 0.37 0.48 332\n",
" 56 0.72 0.51 0.59 460\n",
" 57 0.40 0.22 0.28 400\n",
" 58 0.57 0.28 0.38 515\n",
" 59 0.79 0.60 0.68 479\n",
" 60 0.35 0.09 0.14 516\n",
" 61 0.54 0.39 0.45 496\n",
" 62 0.69 0.62 0.65 304\n",
" 63 0.36 0.19 0.25 1459\n",
" 64 0.44 0.27 0.33 616\n",
" 65 0.66 0.41 0.51 399\n",
" 66 0.75 0.56 0.64 1044\n",
" 67 0.58 0.52 0.55 1046\n",
" 68 0.56 0.31 0.40 976\n",
" 69 0.72 0.60 0.65 3891\n",
" 70 0.64 0.44 0.52 333\n",
" 71 0.71 0.53 0.61 2201\n",
" 72 0.54 0.31 0.40 304\n",
" 73 0.51 0.22 0.31 334\n",
" 74 0.58 0.39 0.47 520\n",
" 75 0.67 0.34 0.45 411\n",
" 76 0.31 0.04 0.07 388\n",
" 77 0.51 0.34 0.41 296\n",
" 78 0.48 0.16 0.24 314\n",
" 79 0.83 0.63 0.72 886\n",
" 80 0.68 0.61 0.65 495\n",
" 81 0.66 0.46 0.54 326\n",
" 82 0.70 0.53 0.60 329\n",
" 83 0.80 0.50 0.61 473\n",
" 84 0.47 0.22 0.30 728\n",
" 85 0.75 0.66 0.70 3314\n",
" 86 0.75 0.60 0.66 1918\n",
" 87 0.21 0.01 0.03 296\n",
" 88 0.68 0.51 0.58 2144\n",
" 89 0.66 0.46 0.55 600\n",
" 90 0.58 0.37 0.45 481\n",
" 91 0.62 0.42 0.50 887\n",
" 92 0.44 0.14 0.21 444\n",
" 93 0.32 0.05 0.09 426\n",
" 94 0.33 0.01 0.02 323\n",
" 95 0.60 0.41 0.49 814\n",
" 96 0.96 0.95 0.96 898\n",
" 97 0.79 0.79 0.79 685\n",
" 98 0.91 0.88 0.89 759\n",
" 99 0.62 0.56 0.59 440\n",
" 100 0.49 0.20 0.29 1563\n",
" 101 0.42 0.11 0.17 428\n",
" 102 0.61 0.44 0.51 1498\n",
" 103 0.59 0.13 0.22 412\n",
" 104 0.35 0.08 0.14 733\n",
" 105 0.52 0.27 0.35 566\n",
" 106 0.59 0.35 0.44 607\n",
" 107 0.35 0.08 0.12 1137\n",
" 108 0.36 0.05 0.09 505\n",
" 109 0.78 0.56 0.65 175\n",
" 110 0.67 0.53 0.59 279\n",
" 111 0.78 0.61 0.68 299\n",
" 112 0.52 0.47 0.49 323\n",
" 113 0.54 0.27 0.36 256\n",
" 114 0.66 0.58 0.62 1654\n",
" 115 0.53 0.29 0.38 1247\n",
" 116 0.43 0.25 0.32 1473\n",
" 117 0.52 0.33 0.41 1450\n",
" 118 0.82 0.69 0.75 393\n",
" 119 0.11 0.01 0.01 596\n",
" 120 0.46 0.25 0.33 1876\n",
" 121 0.75 0.64 0.69 853\n",
" 122 0.40 0.13 0.19 1010\n",
" 123 0.43 0.07 0.12 306\n",
" 124 0.77 0.78 0.78 632\n",
" 125 0.58 0.38 0.46 1941\n",
" 126 0.37 0.13 0.19 1115\n",
" 127 0.34 0.08 0.13 1359\n",
" 128 0.79 0.82 0.81 774\n",
" 129 0.89 0.91 0.90 699\n",
" 130 0.88 0.85 0.86 305\n",
" 131 0.43 0.16 0.23 353\n",
" 132 0.43 0.16 0.24 659\n",
" 133 0.43 0.25 0.32 413\n",
" 134 0.60 0.42 0.49 2650\n",
" 135 0.40 0.15 0.22 640\n",
" 136 0.51 0.41 0.46 224\n",
" 137 0.55 0.36 0.44 2125\n",
" 138 0.24 0.09 0.13 309\n",
"\n",
" micro avg 0.67 0.47 0.55 141154\n",
" macro avg 0.58 0.39 0.45 141154\n",
"weighted avg 0.63 0.47 0.53 141154\n",
" samples avg 0.67 0.49 0.54 141154\n",
"\n",
"Pipeline complete\n",
"/home/lily/br384/anaconda3/envs/EHRKit/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"# Create and train mimic data preprocessing and model, default is a random forest model\n",
"# But any model can be passed in (SKlearn or other model which fits and predicts in the same manner)\n",
"# Note that the resulting classification report identifies the metrics for each of the 138 different icd9 codes that this model investigates.\n",
"print(\"Building basic tfidf pipeline\")\n",
"from sklearn.neural_network import MLPClassifier\n",
"clf = MLPClassifier(hidden_layer_sizes=(100,), max_iter=100, verbose=True)\n",
"# Switch max_iter to 100 for better results, but to run for the first time 10 is good\n",
"my_mimic_pipeline = codingPipeline(verbose=True, model=clf, data_path = OUTPUT_DATA_PATH)\n",
"print(\"Pipeline complete\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Auroc is 0.69\n"
]
}
],
"source": [
"# Let's check out the auroc\n",
"auroc = my_mimic_pipeline.auroc\n",
"print(\"Auroc is {:.2f}\".format(auroc))"
]
},
{
"source": [
"## Let's test out the model on a specific note!"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Here we load the data into the pipeline, this function simply saves the data, we don't want to save the data automatically because it uses more memory\n",
"my_mimic_pipeline.load_data()\n",
"df = my_mimic_pipeline.data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predicted ICD9 codes: [('424', '441', '785')]\nTrue ICD9 codes: ['424', '441', '785']\n"
]
}
],
"source": [
"# We run the algorithm and see that at least for this example our model is pretty good\n",
"pred = my_mimic_pipeline.predict(df['TEXT'].iloc[10])\n",
"true = df['TARGET'].iloc[10]\n",
"print(\"Predicted ICD9 codes: {}\".format(pred))\n",
"print(\"True ICD9 codes: {}\".format(true))"
]
}
]
}