This repository implements a high-level wrapper using Python3 and PyTorch of methods described in the paper Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework.

The project was developed by Professor Meliha Yetisgen and Dr. Kevin Lybarger of UW-BioNLP and collaborators at the University of Washington.

• Input - Chest X-Ray findings as .txt files
• Output - Predictions for ARDS-related information as .json files

The main.py script provides a convenient means of applying the classifier on a directory of Chest X-ray notes as .txt files. Running the tool produces an output for each Chest X-ray note that

1. Predicts opacities (increased radiodensity)
2. Classifies them as parenchymal (indicative of alveolar edema/infiltrates) or extraparenchymal (outside the lungs or not indicative of alveolar edema/infiltrates)
3. Resolves laterality (unilateral or bilateral)

The .json output for each document's predictions is of the form:

{
    "infiltrates": "none" "present" "unilateral" "bilateral",
    "extraparenchymal": "none" "present" "unilateral" "bilateral"
}

• Python version >= 3.6
• PyTorch version >= 1.6.0
• Transformers >= 4.0.1

See requirements.txt for additional dependencies.

1. Clone the repository

$ git clone [email protected]:uw-bionlp/ards.git
$ cd /ards

2. As the trained model for this code is not included in the repository, please contact Professor Meliha Yetisgen [email protected] to gain access to the model. Copy the model files to /ards/model.

3. Install Python3 dependencies. We recommend doing so using a virtual environment:

$ python3 -m venv venv
$ source venv/bin/activate
$ python3 -m pip install -r requirements.txt

4. Download the spaCy english language model

$ python3 -m spacy download en

5. Run the tool on a directory of Chest X-ray notes as .txt files:

$ python3 main.py <your_notes_directory>

You can also specify the additional parameters

• --output_path, which outputs your files to a specify location (rather than a default /output/<dir_name><current_timestamp> directory)
• --output_single_file, which can alternatively output a single .jsonl file with each row in the file representing an input file's output
• --batch_size, which specifies the number of note batches to predict at a time. If unspecified, this defaults to 8.