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A set of spaCy components to extract information from clinical notes written in French

Project description

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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!

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" page in the documentation.

Quick start


You can install EDS-NLP via pip:

pip install edsnlp

We recommend pinning the library version in your projects, or use a strict package manager like Poetry.

pip install edsnlp==0.8.1

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 spacy

nlp = spacy.blank("fr")

terms = dict(
    covid=["covid", "coronavirus"],

# Sentencizer component, needed for negation detection
# Matcher component
nlp.add_pipe("eds.matcher", config=dict(terms=terms))
# Negation detection

# Process your text in one call !
doc = nlp("Le patient est atteint de covid")

# Out: (covid,)

# Out: False


Go to the documentation for more information.


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.


If you use EDS-NLP, please cite us as below.

  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    = {}


We would like to thank Assistance Publique – Hôpitaux de Paris, AP-HP Foundation and Inria for funding this project.

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