zensols.mednlp 1.6.0

A natural language medical domain parsing library.

Medical natural language parsing and utility library

A natural language medical domain parsing library. This library:

• Provides an interface to the UTS (UMLS Terminology Services) RESTful service with data caching (NIH login needed).
• Wraps the MedCAT library by parsing medical and clinical text into first class Python objects reflecting the structure of the natural language complete with UMLS entity linking with CUIs and other domain specific features.
• Combines non-medical (such as POS and NER tags) and medical features (such as CUIs) in one API and resulting data structure and/or as a Pandas data frame.
• Provides cui2vec as a word embedding model for either fast indexing and access or to use directly as features in a Zensols Deep NLP embedding layer model.
• Provides access to cTAKES using as a dictionary like Stash abstraction.
• Includes a command line program to access all of these features without having to write any code.

Documentation

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

Obtaining

The easiest way to install the command line program is via the pip installer.

pip3 install zensols.mednlp

Binaries are also available on pypi.

Usage

To parse text, create features, and extract clinical concept identifiers:

>>> from zensols.mednlp import ApplicationFactory
>>> doc_parser = ApplicationFactory.get_doc_parser()
>>> doc = doc_parser('John was diagnosed with kidney failure')
>>> for tok in doc.tokens: print(tok.norm, tok.pos_, tok.tag_, tok.cui_, tok.detected_name_)
John PROPN NNP -<N>- -<N>-
was AUX VBD -<N>- -<N>-
diagnosed VERB VBN -<N>- -<N>-
with ADP IN -<N>- -<N>-
kidney NOUN NN C0035078 kidney~failure
failure NOUN NN C0035078 kidney~failure
>>> print(doc.entities)
(<John>, <kidney failure>)

See the full example, and for other functionality, see the examples.

MedCAT Models

By default, this library the small MedCAT model used for tutorials, and is not sufficient for any serious project. To get the UMLS trained model,the MedCAT UMLS request form from be filled out (see the MedCAT repository).

After you obtain access and download the new model, add the following to ~/.mednlprc with the following:

[medcat_status_resource]
url = file:///location/to/the/downloaded/file/umls_sm_wstatus_2021_oct.zip'

Attribution

This API utilizes the following frameworks:

• MedCAT: used to extract information from Electronic Health Records (EHRs) and link it to biomedical ontologies like SNOMED-CT and UMLS.
• cTAKES: a natural language processing system for extraction of information from electronic medical record clinical free-text.
• cui2vec: a new set of (like word) embeddings for medical concepts learned using an extremely large collection of multimodal medical data.
• Zensols Deep NLP library: a deep learning utility library for natural language processing that aids in feature engineering and embedding layers.
• ctakes-parser: parses cTAKES output in to a Pandas data frame.

Citation

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

@inproceedings{landes-etal-2023-deepzensols,
    title = "{D}eep{Z}ensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility",
    author = "Landes, Paul  and
      Di Eugenio, Barbara  and
      Caragea, Cornelia",
    editor = "Tan, Liling  and
      Milajevs, Dmitrijs  and
      Chauhan, Geeticka  and
      Gwinnup, Jeremy  and
      Rippeth, Elijah",
    booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
    month = dec,
    year = "2023",
    address = "Singapore, Singapore",
    publisher = "Empirical Methods in Natural Language Processing",
    url = "https://aclanthology.org/2023.nlposs-1.16",
    pages = "141--146"
}

Community

Please star the project and let me know how and where you use this API. Contributions as pull requests, feedback and any input is welcome.

Changelog

An extensive changelog is available here.

License

MIT License

Copyright (c) 2021 - 2023 Paul Landes