A Label Attention Model for ICD Coding from Clinical Text

This project provides the code for our JICAI 2020 A Label Attention Model for ICD Coding from Clinical Text paper.

The general architecture and experimental results can be found in our paper:

  @inproceedings{ijcai2020-461-vu,
      title     = {A Label Attention Model for ICD Coding from Clinical Text},
      author    = {Vu, Thanh and Nguyen, Dat Quoc and Nguyen, Anthony},
      booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},             
      pages     = {3335--3341},
      year      = {2020},
      month     = {7},
      note      = {Main track}
      doi       = {10.24963/ijcai.2020/461},
      url       = {https://doi.org/10.24963/ijcai.2020/461},
   }

Please CITE our paper when this code is used to produce published results or incorporated into other software.

Requirements

python>=3.6
torch==1.4.0
scikit-learn==0.23.1
numpy==1.16.3
scipy==1.2.1
pandas==0.24.2
tqdm==4.31.1
nltk>=3.4.5
psycopg2==2.7.7
gensim==3.6.0
transformers==2.11.0

Run pip install -r requirements.txt to install the required libraries

Run python3 and run import nltk and nltk.download('punkt') for tokenization

Data preparation

MIMIC-III-full and MIMIC-III-50 experiments

data/mimicdata/mimic3

The id files are from caml-mimic
Install the MIMIC-III database with PostgreSQL following this instruction
Generate the train/valid/test sets using src/util/mimiciii_data_processing.py. (Configure the connection to PostgreSQL at Line 139)

MIMIC-II-full experiment

data/mimicdata/mimic2

Place the MIMIC-II file (MIMIC_RAW_DSUMS) to data/mimicdata/mimic2
Generate the train/valid/test sets using src/util/mimicii_data_processing.py.

Note that: The code will generate 3 files (train.csv, valid.csv, and test.csv) for each experiment.

Pretrained word embeddings

data/embeddings

We used gensim to train the embeddings (word2vec model) using the entire MIMIC-III discharge summary data.

Our code also supports subword embeddings (fastText) which helps produce better performances (see src/args_parser.py).

How to run

The problem and associated configurations are defined in configuration/config.json. Note that there are 3 files in each data folder (train.csv, valid.csv and test.csv)

There are common hyperparameters for all the models and the model-specific hyperparameters. See src/args_parser.py for more detail

Here is an example of using the framework on MIMIC-III dataset (full codes) with hierarchical join learning

python -m src.run \
    --problem_name mimic-iii_2_full \
    --max_seq_length 4000 \
    --n_epoch 50 \
    --patience 5 \
    --batch_size 8 \
    --optimiser adamw \
    --lr 0.001 \
    --dropout 0.3 \
    --level_projection_size 128 \
    --main_metric micro_f1 \
    --embedding_mode word2vec \
    --embedding_file data/embeddings/word2vec_sg0_100.model \
    --attention_mode label \
    --d_a 512 \
    RNN  \
    --rnn_model LSTM \
    --n_layers 1 \
    --bidirectional 1 \
    --hidden_size 512

yacaikk / LAAT