# LAAT
**Repository Path**: yacaikk/LAAT
## Basic Information
- **Project Name**: LAAT
- **Description**: A Label Attention Model for ICD Coding from Clinical Text
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-19
- **Last Updated**: 2021-03-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 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](https://arxiv.org/abs/2007.06351) paper.
The general architecture and experimental results can be found in our [paper](https://arxiv.org/abs/2007.06351):
```
@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](https://github.com/jamesmullenbach/caml-mimic)
- Install the MIMIC-III database with PostgreSQL following this [instruction](https://mimic.physionet.org/tutorials/install-mimic-locally-ubuntu/)
- 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](https://archive.physionet.org/works/ICD9CodingofDischargeSummaries/)) 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
```