
[](https://aphp.github.io/edsnlp/latest/)
[](https://pypi.org/project/edsnlp/)
[](https://aphp.github.io/edsnlp/demo/)
[](https://codecov.io/gh/aphp/edsnlp)
[](https://zenodo.org/badge/latestdoi/467585436)
# EDS-NLP
EDS-NLP provides a set of spaCy components that are used to extract information from clinical notes written in French.
Check out the interactive [demo](https://aphp.github.io/edsnlp/demo/)!
If it's your first time with spaCy, we recommend you familiarise yourself with some of their key concepts by looking at the "[spaCy 101](https://aphp.github.io/edsnlp/latest/tutorials/spacy101/)" page in the documentation.
## Quick start
### Installation
You can install EDS-NLP via `pip`:
```shell
pip install edsnlp
```
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==0.7.4
```
### 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 spacy
nlp = spacy.blank("fr")
terms = dict(
covid=["covid", "coronavirus"],
)
# Sentencizer component, needed for negation detection
nlp.add_pipe("eds.sentences")
# Matcher component
nlp.add_pipe("eds.matcher", config=dict(terms=terms))
# Negation detection
nlp.add_pipe("eds.negation")
# Process your text in one call !
doc = nlp("Le patient est atteint de covid")
doc.ents
# Out: (covid,)
doc.ents[0]._.negation
# Out: False
```
## Documentation
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 = {Dura, Basile and Wajsburt, Perceval and Petit-Jean, Thomas 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 = {http://aphp.github.io/edsnlp}
}
```
## Acknowledgement
We would like to thank [Assistance Publique – Hôpitaux de Paris](https://www.aphp.fr/) and [AP-HP Foundation](https://fondationrechercheaphp.fr/) for funding this project.