zensols.mednlp 1.8.0
A natural language medical domain parsing library.
Keywords: tooling

# Medical natural language parsing and utility library

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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](https://plandes.github.io/mednlp/index.html).
The [API reference](https://plandes.github.io/mednlp/api.html) 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](https://figshare.com/ndownloader/files/10959626) 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
>>> 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](example/features/simple.py), and for other
functionality, see the [examples](example).


## MedCAT Models

By default, this library uses the small MedCAT model used for
[tutorials](https://github.com/CogStack/MedCATtutorials/pull/12), 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/2025.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](CHANGELOG.md).


## License

[MIT License](LICENSE.md)

Copyright (c) 2021 - 2024 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.mednlp/
[pypi-link]: https://pypi.python.org/pypi/zensols.mednlp
[pypi-badge]: https://img.shields.io/pypi/v/zensols.mednlp.svg
[python311-badge]: https://img.shields.io/badge/python-3.11-blue.svg
[python311-link]: https://www.python.org/downloads/release/python-3110
[build-badge]: https://github.com/plandes/mednlp/workflows/CI/badge.svg
[build-link]: https://github.com/plandes/mednlp/actions

[MedCAT]: https://github.com/CogStack/MedCAT
[MedCAT UMLS request form]: https://uts.nlm.nih.gov/uts/login?service=https:%2F%2Fmedcat.rosalind.kcl.ac.uk%2Fauth-callback

[Pandas]: https://pandas.pydata.org
[ctakes-parser]: https://pypi.org/project/ctakes-parser

[UTS]: https://uts.nlm.nih.gov/uts/
[UMLS]: https://www.nlm.nih.gov/research/umls/
[CUIs]: https://www.nlm.nih.gov/research/umls/new_users/online_learning/Meta_005.html
[cui2vec]: https://arxiv.org/abs/1804.01486
[cTAKES]: https://ctakes.apache.org
[word embedding model]: https://plandes.github.io/deepnlp/api/zensols.deepnlp.embed.html#zensols.deepnlp.embed.domain.WordEmbedModel
[Zensols NLP parsing API]: https://plandes.github.io/nlparse/doc/feature-doc.html
[Zensols Deep NLP library]: https://github.com/plandes/deepnlp
[Zensols Deep NLP embedding layer]: https://plandes.github.io/deepnlp/api/zensols.deepnlp.layer.html#zensols.deepnlp.layer.embed.EmbeddingNetworkModule
[Stash]: https://plandes.github.io/util/api/zensols.persist.html#zensols.persist.domain.Stash