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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 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 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 !

• Rule-based components for French clinical notes
• Trainable components: NER, Span classification
• LLM-based components
• Support for multitask deep-learning models with weights sharing
• Fast 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, JSON, Parquet, Pandas or Spark

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==0.19.0

or if you want to use the trainable components (using pytorch)

pip install "edsnlp[ml]==0.19.0"

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, 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

Go to the documentation for more information.

The performances of an extraction pipeline may depend on the population and documents that are considered.

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.

@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}
}

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