Medical natural language parsing and utility library

A natural language medical domain parsing library. This library:

• Provides an interface to the UTS 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.

Installing

Install the library using a Python package manager such as pip: bash pip3 install zensols.mednlp

CUI Embeddings

To use the cui2vec to functionality, the embeddings must be manually downloaded. Start with this commands: bash mkdir -p ~/.cache/zensols/mednlp wget -O ~/.cache/zensols/mednlp/cui2vec.zip https://figshare.com/ndownloader/files/10959626?private_link=00d69861786cd0156d81 If the download fails or the file is not a zip file (rather an HTML error message text), then you will need to download the file manually by browsing to the file, and then moving it to ~/.cache/zensols/mednlp/cui2vec.zip.

Usage

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

from zensols.mednlp import ApplicationFactory docparser = ApplicationFactory.getdocparser() doc = docparser('John was diagnosed with kidney failure') for tok in doc.tokens: print(tok.norm, tok.pos, tok.tag, tok.cui, tok.detectedname_) John PROPN NNP -- -- was AUX VBD -- -- diagnosed VERB VBN -- -- with ADP IN -- -- kidney NOUN NN C0035078 kidney~failure failure NOUN NN C0035078 kidney~failure print(doc.entities) (, ) ``` See the full example, and for other functionality, see the examples.

MedCAT Models

By default, this library uses 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:

ini [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:

bibtex @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 = "Association for Computational Linguistics", 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 - 2025 Paul Landes