CheXpert NLP tool to extract observations from radiology reports.
Read more about our project here and our AAAI 2019 paper here.
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Clone the NegBio repository:
git clone git@github.com:ncbi-nlp/NegBio.git
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Add the NegBio directory to your
PYTHONPATH
:export PYTHONPATH={path to negbio directory}:$PYTHONPATH
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Make the virtual environment:
conda env create -f environment.yml
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Install NLTK data:
python -m nltk.downloader universal_tagset punkt wordnet
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Download the
GENIA+PubMed
parsing model:
>>> from bllipparser import RerankingParser
>>> RerankingParser.fetch_and_load('GENIA+PubMed')
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.
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.
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}
}