chexpert-labeler

CheXpert NLP tool to extract observations from radiology reports.

Read more about our project here and our AAAI 2019 paper here.

Prerequisites

1. Clone the NegBio repository:

git clone https://github.com/ncbi-nlp/NegBio.git

2. Add the NegBio directory to your PYTHONPATH:

export PYTHONPATH={path to negbio directory}:$PYTHONPATH

3. Make the virtual environment:

conda env create -f environment.yml

4. Activate the virtual environment:

conda activate chexpert-label

5. Install NLTK data:

python -m nltk.downloader universal_tagset punkt wordnet

6. Download the GENIA+PubMed parsing model:

>>> from bllipparser import RerankingParser
>>> RerankingParser.fetch_and_load('GENIA+PubMed')

Usage

Place reports in a headerless, single column csv {reports_path}. Each report must be contained in quotes if (1) it contains a comma or (2) it spans multiple lines. See sample_reports.csv (with output labeled_reports.csv)for an example.

python label.py --reports_path {reports_path}

Run python label.py --help for descriptions of all of the command-line arguments.

Contributions

This repository builds upon the work of NegBio.

This tool was developed by Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, and Silviana Ciurea-Ilcus.

Citing

If you're using the CheXpert labeling tool, please cite this paper:

@inproceedings{irvin2019chexpert,
  title={CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Thirty-Third AAAI Conference on Artificial Intelligence},
  year={2019}
}