A set of spaCy components to extract information from clinical notes written in French
Project description
EDS-NLP
EDS-NLP is a collaborative NLP framework that aims 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 to represent documents and their annotations, and Pytorch 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 pipelines, 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 !
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.
pip install edsnlp==0.10.0beta1
or if you want to use the trainable components (using pytorch)
pip install "edsnlp[ml]==0.10.0beta1"
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.
import edsnlp
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", config=dict(terms=terms))
# Negation detection
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
Go to the documentation 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 for detail.
Citation
If you use EDS-NLP, please cite us as below.
@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, AP-HP Foundation and Inria for funding this project.
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