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EMR Coding with Semi-Parametric Multi-Head Matching Networks

Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this repository, we provide the code for a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models for coding EMRs.

Note: Examples of the data format can be found in the "data" folder.

Required Packages

  • Python 2.7
  • numpy 1.11.1+
  • scipy 0.18.0+
  • Theano
  • gensim
  • sklearn
  • nltk

Usage

Training

python train_match.py --num_epochs 25 --word_vectors 'gensim_w2v_pubmed' --model_type cnn --train_data_X './data/train_data.json' --val_data_X './data/dev_data.json' --checkpoint_dir './checkpoints' --num_feat_maps 300 --grad_clip 3 --min_df 5 --lr 0.0001 --penalty 0.0000 --dropout 0.5 --lr_decay 0.0000 --cnn_conv_size 3 4 5  --checkpoint_name my_model_name

usage: train_match.py [-h] [--num_epochs NUM_EPOCHS] [--num_models NUM_MODELS]
                      [--word_vectors WORD_VECTORS] [--labels LABELS]
                      [--checkpoint_dir CHECKPOINT_DIR]
                      [--checkpoint_name CHECKPOINT_NAME]
                      [--hidden_state HIDDEN_STATE]
                      [--learn_embeddings LEARN_EMBEDDINGS] [--min_df MIN_DF]
                      [--lr LR] [--penalty PENALTY] [--dropout DROPOUT]
                      [--lr_decay LR_DECAY] [--minibatch_size MINIBATCH_SIZE]
                      [--val_minibatch_size VAL_MINIBATCH_SIZE]
                      [--model_type MODEL_TYPE] [--train_data_X TRAIN_DATA_X]
                      [--val_data_X VAL_DATA_X] [--seed SEED]
                      [--grad_clip GRAD_CLIP]
                      [--cnn_conv_size CNN_CONV_SIZE [CNN_CONV_SIZE ...]]
                      [--num_feat_maps NUM_FEAT_MAPS] [--num_att NUM_ATT]
                      [--num_support NUM_SUPPORT]

Train Neural Network.

optional arguments:
  -h, --help            show this help message and exit
  --num_epochs NUM_EPOCHS
                        Number of updates to make.
  --num_models NUM_MODELS
                        Number of updates to make.
  --word_vectors WORD_VECTORS
                        Word vecotors filepath.
  --labels LABELS       All Labels.
  --checkpoint_dir CHECKPOINT_DIR
                        Checkpoint directory.
  --checkpoint_name CHECKPOINT_NAME
                        Checkpoint File Name.
  --hidden_state HIDDEN_STATE
                        hidden layer size.
  --learn_embeddings LEARN_EMBEDDINGS
                        Learn Embedding Parameters.
  --min_df MIN_DF       Min word count.
  --lr LR               Learning Rate.
  --penalty PENALTY     Regularization Parameter.
  --dropout DROPOUT     Dropout Value.
  --lr_decay LR_DECAY   Learning Rate Decay.
  --minibatch_size MINIBATCH_SIZE
                        Mini-batch Size.
  --val_minibatch_size VAL_MINIBATCH_SIZE
                        Val Mini-batch Size.
  --model_type MODEL_TYPE
                        Neural Net Architecutre.
  --train_data_X TRAIN_DATA_X
                        Training Data.
  --val_data_X VAL_DATA_X
                        Validation Data.
  --seed SEED           Random Seed.
  --grad_clip GRAD_CLIP
                        Gradient Clip Value.
  --cnn_conv_size CNN_CONV_SIZE [CNN_CONV_SIZE ...]
                        CNN Covolution Sizes (widths)
  --num_feat_maps NUM_FEAT_MAPS
                        Number of CNN Feature Maps.
  --num_att NUM_ATT     Number of Heads.
  --num_support NUM_SUPPORT
                        Number nearest neighbors to sample for each input
                        instance.

Testing

The file "test_match.py" provides an example on how to run and evaluate our method.

python test_match.py --data_X './data/test_data.json' --checkpoint_model './checkpoints/my_model_name.pkl' --train_data_X './data/train_data.json' --minibatch_size 3 --knn 8 --val_minibatch_size 3

usage: test_match.py [-h] [--checkpoint_model CHECKPOINT_MODEL]
                     [--data_X DATA_X] [--minibatch_size MINIBATCH_SIZE]
                     [--val_minibatch_size VAL_MINIBATCH_SIZE] [--knn KNN]
                     [--train_data_X TRAIN_DATA_X]

Test Neural Network.

optional arguments:
  -h, --help            show this help message and exit
  --checkpoint_model CHECKPOINT_MODEL
                        Checkpoint Model.
  --data_X DATA_X       Test/Validation Data.
  --minibatch_size MINIBATCH_SIZE
                        Mini-batch Size.
  --val_minibatch_size VAL_MINIBATCH_SIZE
                        Mini-batch Size.
  --knn KNN             KNN Size.
  --train_data_X TRAIN_DATA_X
                        Training Data.

Acknowledgements

Anthony Rios and Ramakanth Kavuluru. "EMR Coding with Semi-Parametric Multi-Head Matching Networks". NAACL 2018

@inproceedings{arios2018emrmatch,
  title={EMR Coding with Semi-Parametric Multi-Head Matching Networks},
  author={Rios, Anthony and Kavuluru, Ramakanth},
  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year={2018}
}

Written by Anthony Rios (anthonymrios at gmail dot com)