Baseline model for the MedSecId paper

This contains a basic baseline model for identifying section in clinical text from the paper A New Public Corpus for Clinical Section Identification: MedSecId. The purpose of this repository is to provide a means to reproduce the results in the paper. If you want to include this work in your own projects, use the mimicsid package described in the Inclusion in Your Projects section, which was designed to be an off-the-shelf package pip install.

Reproducing the Results

Python 3.9.9 was used with the requirements in src/requirements.txt and src/requirements-mednlp.txt.

To train and test the models, use the run.sh script by:

1. Copy the MIMIC-III NOTEEVENTS.csv file to the corpus directory.
2. Download the annotation set and uncompress it:
   1. pushd corpus
   2. wget https://zenodo.org/record/7150451/files/section-id-annotations.zip
   3. unzip section-id-annotations.zip
   4. popd
3. Remove the repo results: rm -r results
4. Create the Python environment (in pyvirenv): ./run.sh pyenv
5. Install all Python libraries and models: ./run.sh pydep
6. Create the features as mini-batches (takes a while): ./run.sh batch
7. Test and train the models (takes a while): ./run.sh traintest
8. Create the metrics used in the ./run paperresults

At the end of this, there should be a results directory with:

• results/stats: the corpus statistics
• results/perf: the summary of the results and labels of the best model
• results/model: the models and model specific results

Inclusion in Your Projects

The purpose of this repository is to reproduce the results in the paper. If you want to use the annotations and/or use the pretrained model, please refer to the mimicsid repository.

Data Analysis

The medical concept (CUI) plot given in the paper, and others are available as interactive 3D plots here.

Citation

If you use this project in your research please use the following BibTeX entry:

@inproceedings{landes-etal-2022-new,
    title = "A New Public Corpus for Clinical Section Identification: {M}ed{S}ec{I}d",
    author = "Landes, Paul  and
      Patel, Kunal  and
      Huang, Sean S.  and
      Webb, Adam  and
      Di Eugenio, Barbara  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.326",
    pages = "3709--3721"
}

Also please cite the Zensols Framework:

@article{Landes_DiEugenio_Caragea_2021,
  title={DeepZensols: Deep Natural Language Processing Framework},
  url={http://arxiv.org/abs/2109.03383},
  note={arXiv: 2109.03383},
  journal={arXiv:2109.03383 [cs]},
  author={Landes, Paul and Di Eugenio, Barbara and Caragea, Cornelia},
  year={2021},
  month={Sep}
}

License

MIT License

Copyright (c) 2022 Paul Landes