Dependencies

• Python 3.6
• pytorch 0.4.1
• tqdm
• scikit-learn 0.19.1
• numpy 1.13.3, scipy 0.19.1, pandas 0.24.1
• jsonlines

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

Training a new model

Create a mimicdata folder that holds the files D_ICD_DIAGNOSES.csv and D_ICD_PROCEDURES.csv from your MIMIC-III database copy and a saved_models folder that will hold your trained models.

To train a new model use the script training.py. Execute python training.py -h for a full list of input arguments and flags.

Use the following files as input for the model

• notes_labeled_train.ndjson training split of the dataset
• notes_labeled_dev.ndjson development split of the dataset
• notes_labeled_test.ndjson validation split of the dataset
• glove.840B.300d.txt GloVe pre-trained word vectors (available at https://nlp.stanford.edu/data/glove.840B.300d.zip)
• vocab.csv vocabulary of the training corpus

To train a new model run the following command

python3 training.py /path/to/notes_labeled_train.ndjson /path/to/vocab.csv full conv_dilated 300,125,125,125 --filter-size 3 --dilation 1,2,4 --dropout 0.2,0.2,0.2 --n-epochs 200 --patience 10 --lr 0.0001 --criterion f1_micro_fine --batch-size 8 --max-len 5200 --embed-file /path/to/glove.840B.300d.txt --embed-desc --models-dir /path/to/saved_models --data-dir /path/to/mimicdata

To test a pre-trained model run the following command

python3 training.py /path/to/notes_labeled_train.ndjson /path/to/vocab.csv full conv_dilated 300,125,125,125 --filter-size 3 --dilation 1,2,4 --dropout 0.2,0.2,0.2 --n-epochs 200 --patience 10 --lr 0.0001 --criterion f1_micro_fine --batch-size 8 --max-len 5200 --embed-file /path/to/glove.840B.300d.txt --embed-desc --models-dir /path/to/saved_models --data-dir /path/to/mimicdata --test-model /path/to/model.pth