Status: Archived. Code is provided as-is with no updates expected. Unfortunately I don't have the personal time to dedicate to maintaining this repo/responding to issues, nor access to the MIMIC dataset anymore, though I hope the model code and data splits can still be of use to the community.

Code for the paper Explainable Prediction of Medical Codes from Clinical Text.

• Python 3.6, though 2.7 should hopefully work as well
• pytorch 0.3.0
• tqdm
• scikit-learn 0.19.1
• numpy 1.13.3, scipy 0.19.1, pandas 0.20.3
• jupyter-notebook 5.0.0
• gensim 3.2.0
• nltk 3.2.4

Other versions may also work, but the ones listed are the ones I've used

To get started, first edit constants.py to point to the directories holding your copies of the MIMIC-II and MIMIC-III datasets. Then, organize your data with the following structure:

mimicdata
|   D_ICD_DIAGNOSES.csv
|   D_ICD_PROCEDURES.csv
|   ICD9_descriptions (already in repo)
└───mimic2/
|   |   MIMIC_RAW_DSUMS
|   |   MIMIC_ICD9_mapping
|   |   training_indices.data
|   |   testing_indices.data
└───mimic3/
|   |   NOTEEVENTS.csv
|   |   DIAGNOSES_ICD.csv
|   |   PROCEDURES_ICD.csv
|   |   *_hadm_ids.csv (already in repo)

The MIMIC-II files can be obtained from this repository.

Now, make sure your python path includes the base directory of this repository. Then, in Jupyter Notebook, run all cells (in the menu, click Cell -> Run All) in notebooks/dataproc_mimic_II.ipynb and notebooks/dataproc_mimic_III.ipynb. These will take some time, so go for a walk or bake some cookies while you wait. You can speed it up by skipping the "Pre-train word embeddings" sections.

To directly reproduce the results of the paper, first run the data processing steps above. We provide our pre-trained models for CAML and DR-CAML for the MIMIC-III full-label dataset. They are saved as model.pth in their respective directories. We also provide an evaluate_model.sh script to reproduce our results from the models.

To train a new model from scratch, please use the script learn/training.py. Execute python training.py -h for a full list of input arguments and flags. The train_new_model.sh scripts in the predictions/ subdirectories can serve as examples (or you can run those directly to use the same hyperparameters).

The predictions that provide the results in the paper are provided in predictions/. Each directory contains:

• preds_test.psv, a pipe-separated value file containing the HADM_ID's and model predictions of all testing examples
• train_new_model.sh, which trains a new model with the hyperparameters provided in the paper.

To reproduce our F-measure results from the predictions, for example the CNN results on MIMIC-II, run python get_metrics_for_saved_predictions.py predictions/CNN_mimic2_full.