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Code for the AACL 2022 Paper "This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text"

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ProtoPatient

The ProtoPatient model bases its predictions on similarities to prototypical diagnosis descriptions.

This repository contains source code for the experiments conducted in the paper This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text.

A demo application can be found here: ProtoPatient Demo.

Usage

Install requirements: pip install -r requirements.txt

For training the ProtoPatient model, which achieved the best results in our experiments, run:

python training.py 
            --model_type PROTO
            --train_file {TRAIN.csv}
            --val_file {VAL.csv}
            --test_file {TEST.csv}
            --num_warmup_steps 5000
            --num_training_steps 5000
            --lr_features 0.000005
            --lr_prototypes 0.001
            --lr_others 0.001
            --use_attention True
            --reduce_hidden_size 256
            --all_labels_path {ALL_LABELS.txt}

To train the PubMedBERT baseline with the best hyperparameters, use the following command:

python training.py 
            --model_type BERT
            --train_file {TRAIN.csv}
            --val_file {VAL.csv}
            --test_file {TEST.csv}
            --num_warmup_steps 1000
            --num_training_steps 5000
            --lr_features 0.00005
            --all_labels_path {ALL_LABELS.txt}

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Code for the AACL 2022 Paper "This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text"

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