Provenience of discharge summaries Pythonic access

This library provides integrated MIMIC-III with discharge summary provenance of data annotations and Pythonic classes. This is a package meant for other researchers based on the paper Hospital Discharge Summarization Data Provenance.

Documentation

See the full documentation. The API reference is also available.

Installation and Configuration

Both the package and database must be installed.

Package

The library can be installed with pip from the pypi repository:

pip3 install --use-deprecated=legacy-resolver zensols.dsprov

The --use-deprecated=legacy-resolver is needed for the spaCy 3.2 dependency. Python version 3.10 must also be used since it depends on PyTorch 1.13 (at least for the binary packages).

Database

The MIMIC-III database must be installed and configured as documented in the mimic package's configuration, which is necessary to render the EHR data used by the annotations. However, instead of using ~/.mimicrc, the file must be called ~/.dsprovrc if not explicitly set with the --config option.. The mimic package's configuration section also provides instructions on how to install a MIMIC-III database via PostgreSQL, SQLite or in a Docker container.

For example, to store cached files in ~/.dsprov/cache using a the SQLite MIMIC-III database file ~/.dsprov/cache/mimic3.sqlite3, your ~/.dsprovrc would be:

[default]
# the directory where cached data is stored
data_dir = ~/.dsprov/cache

[mimic_sqlite_conn_manager]
# location of the MIMIC-III SQLite database file
db_file = path: ~/.dsprov/cache/mimic3.sqlite3

Usage

The package includes a command line interface, which is probably most useful by dumping selected admission annotations.

Command line

# help
$ dsprov --help

# get two admission IDs (hadm_id)
$ dsprov ids -l 2

# print out two admissions
$ dsprov show -l 2

# print out admissions 139676
$ dsprov show -d 139676

# output the JSON of two admissions with indent 4
$ dsprov show -i 4 -f json -d $(dsprov ids -l 2 | awk '{print $1}' | paste -s -d, -)

API

The package can be used directly in your research to provide Python object oriented access to the annotations:

>>> from zensols.nlp import FeatureDocument
>>> from zensols.dsprov import ApplicationFactory, AdmissionMatch
>>> stash = ApplicationFactory.get_stash()
>>> am: AdmissionMatch = next(iter(stash.values()))
>>> doc: FeatureDocument = am.note_matches[0].discharge_summary.note.doc
>>> print(f'hadm: {am.hadm_id}')
>>> print(f'sentences: {len(doc.sents)}')
>>> print(f'tokens: {doc.token_len}')
>>> print(f'entities: {doc.entities}')
hadm: 120334
sentences: 46
tokens: 1039
entities: (<Admission>, <Date>, <Discharge>, <Date>, <Date of Birth>, <Sex>, ...)

Docker

A docker image is available as well.

To use the docker image, do the following:

1. Create (or obtain) the Postgres docker image
2. Clone this repository git clone --recurse-submodules https://github.com/plandes/dsprov
3. Set the working directory to the repo: cd dsprov
4. Copy the configuration from the installed mimicdb image configuration: make -C docker/mimicdb SRC_DIR=<cloned mimicdb directory> cpconfig
5. Start the container: make -C docker/app up
6. Test sectioning a document: make -C docker/app testdumpsec
7. Log in to the container: make -C docker/app devlogin

Citation

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

@inproceedings{landesHospitalDischargeSummarization2023,
  title = {Hospital {{Discharge Summarization Data Provenance}}},
  booktitle = {The 22nd {{Workshop}} on {{Biomedical Natural Language Processing}} and {{BioNLP Shared Tasks}}},
  author = {Landes, Paul and Chaise, Aaron and Patel, Kunal and Huang, Sean and Di Eugenio, Barbara},
  date = {2023-07},
  pages = {439--448},
  publisher = {{Association for Computational Linguistics}},
  location = {{Toronto, Canada}},
  url = {https://aclanthology.org/2023.bionlp-1.41},
  urldate = {2023-07-10},
  eventtitle = {{{BioNLP}} 2023}
}

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}
}

Changelog

An extensive changelog is available here.

License

MIT License

Copyright (c) 2023 Paul Landes