# Automatic Assignment of ICD codes

## Introduction

This repo contains codes for assignment of ICD codes to medical/clinical text. Data used here is the MIMICIII dataset. Different models have been tried from linear machine learning models to state of the art pretrained NLP model BERT.

## Structure of the project

At the root of the project, you will have:

- **main.py**: used for training and testing different models

- **requirements.txt**: contains the minimum dependencies for running the project

- **w2vmodel.model**: gensim word2vec model trained on MIMICIII discharge summaries

- **src**: a folder that contains:

  - **bert**: contains utilities and files for pretrained bert model

  - **cnn**: contains utilities and files for CNN model

  - **hybrid**: contains utilities and files for the hybrid model (LSTM+CNN) model

  - **rnn**: contains utilities and files for LSTM and GRU models

  - **ovr**: contains utilities and files for different Machine Learning Models (like LR, SVM, NaiveBayes)

  - **fit.py**: training code for both LSTM and CNN models

  - **test_results.py**: inferencing code for trained model used for both LSTM and CNN models

  - **utils.py**: genearal utility codes used for all the models

## Dependencies

 The dependencies are mentioned in the `requirements.txt` file.

 They can be installed by:

 ```bash

pip install -r requirements.txt

```

## How to use the code

Launch train.py with the following arguments:

- `train_path`: path of the training data. 

- `test_path`: path of the test data

- `model_name`: one of the 5 models implemented ['bert', 'hybrid', 'lstm', 'gru', 'cnn', 'ovr']. Default to 'bert'

- `icd_type`: training on different types of icd labels, ['icd9cat', 'icd9code', 'icd10cat', 'icd10code']. Default to 'icd9cat'

- `epochs`: number of epochs 

- `batch_size`: batch size, default to 16 (for bert model).

- `val_split`: validation split of the training data, default = 2/7 (train:val:split = 5:2:3)

- `learning_rate`: default to 2e-5 (for bert model)

- `w2vmodel`: path for pretrained gensim word2vec model.

***Example***

```bash

python main.py --train_path train.csv --test_path test.csv --model_name cnn

```

## Data

The data used for training can be downloaded from:

- [train data](https://drive.google.com/file/d/1--ZVpt614neHN9erxmsg6s6aGInThJ22/view?usp=sharing)

- [test data](https://drive.google.com/file/d/1-4tp0og0I7KyNMoqF2_t1smu0_GqQCVf/view?usp=sharing)