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https://github.com/aphp/edsnlp
Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.
https://github.com/aphp/edsnlp
clinical-data-warehouse deep-learning fast french medical multi-task nlp pytorch rule-based spacy text-mining
Last synced: about 13 hours ago
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Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.
- Host: GitHub
- URL: https://github.com/aphp/edsnlp
- Owner: aphp
- License: bsd-3-clause
- Created: 2022-03-08T16:13:26.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-09-13T09:27:00.000Z (12 days ago)
- Last Synced: 2024-09-21T09:34:06.612Z (4 days ago)
- Topics: clinical-data-warehouse, deep-learning, fast, french, medical, multi-task, nlp, pytorch, rule-based, spacy, text-mining
- Language: Python
- Homepage: https://aphp.github.io/edsnlp/
- Size: 87.1 MB
- Stars: 112
- Watchers: 10
- Forks: 29
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- Contributing: contributing.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://aphp.github.io/edsnlp/latest/)
[](https://pypi.org/project/edsnlp/)
[](https://aphp.github.io/edsnlp/demo/)
[](https://raw.githubusercontent.com/aphp/edsnlp/coverage/coverage.txt)
[](https://zenodo.org/badge/latestdoi/467585436)
EDS-NLP
=======
EDS-NLP is a collaborative NLP framework that aims primarily at extracting information from French clinical notes.
At its core, it is a collection of components or pipes, either rule-based functions or
deep learning modules. These components are organized into a novel efficient and modular pipeline system, built for hybrid and multitask models. We use [spaCy](https://spacy.io) to represent documents and their annotations, and [Pytorch](https://pytorch.org/) as a deep-learning backend for trainable components.
EDS-NLP is versatile and can be used on any textual document. The rule-based components are fully compatible with spaCy's components, and vice versa. This library is a product of collaborative effort, and we encourage further contributions to enhance its capabilities.
Check out our interactive [demo](https://aphp.github.io/edsnlp/demo/) !
## Features
- [Rule-based components](https://aphp.github.io/edsnlp/latest/pipes/) for French clinical notes
- [Trainable components](https://aphp.github.io/edsnlp/latest/pipes/trainable): NER, Span classification
- Support for multitask deep-learning models with [weights sharing](https://aphp.github.io/edsnlp/latest/concepts/torch-component/#sharing-subcomponents)
- [Fast inference](https://aphp.github.io/edsnlp/latest/concepts/inference/), with multi-GPU support out of the box
- Easy to use, with a spaCy-like API
- Compatible with rule-based spaCy components
- Support for various io formats like [BRAT](https://aphp.github.io/edsnlp/latest/data/standoff/), [JSON](https://aphp.github.io/edsnlp/latest/data/json/), [Parquet](https://aphp.github.io/edsnlp/latest/data/parquet/), [Pandas](https://aphp.github.io/edsnlp/latest/data/pandas/) or [Spark](https://aphp.github.io/edsnlp/latest/data/spark/)
## Quick start
### Installation
You can install EDS-NLP via `pip`. We recommend pinning the library version in your projects, or use a strict package manager like [Poetry](https://python-poetry.org/).
```shell
pip install edsnlp==0.13.0
```
or if you want to use the trainable components (using pytorch)
```shell
pip install "edsnlp[ml]==0.13.0"
```
### A first pipeline
Once you've installed the library, let's begin with a very simple example that extracts mentions of COVID19 in a text, and detects whether they are negated.
```python
import edsnlp, edsnlp.pipes as eds
nlp = edsnlp.blank("eds")
terms = dict(
covid=["covid", "coronavirus"],
)
# Split the documents into sentences, this isneeded for negation detection
nlp.add_pipe(eds.sentences())
# Matcher component
nlp.add_pipe(eds.matcher(terms=terms))
# Negation detection (we also support spacy-like API !)
nlp.add_pipe("eds.negation")
# Process your text in one call !
doc = nlp("Le patient n'est pas atteint de covid")
doc.ents
# Out: (covid,)
doc.ents[0]._.negation
# Out: True
```
## Documentation & Tutorials
Go to the [documentation](https://aphp.github.io/edsnlp) for more information.
## Disclaimer
The performances of an extraction pipeline may depend on the population and documents that are considered.
## Contributing to EDS-NLP
We welcome contributions ! Fork the project and propose a pull request.
Take a look at the [dedicated page](https://aphp.github.io/edsnlp/latest/contributing/) for detail.
## Citation
If you use EDS-NLP, please cite us as below.
```bibtex
@misc{edsnlp,
author = {Wajsburt, Perceval and Petit-Jean, Thomas and Dura, Basile and Cohen, Ariel and Jean, Charline and Bey, Romain},
doi = {10.5281/zenodo.6424993},
title = {EDS-NLP: efficient information extraction from French clinical notes},
url = {https://aphp.github.io/edsnlp}
}
```
## Acknowledgement
We would like to thank [Assistance Publique – Hôpitaux de Paris](https://www.aphp.fr/), [AP-HP Foundation](https://fondationrechercheaphp.fr/) and [Inria](https://www.inria.fr) for funding this project.