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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 ordeep learning modules. These components are organized into a novel efficient and modular pipeline system, built for hybrid and multitask models. We usespaCy to represent documents and their annotations, andPytorch 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 interactivedemo !

• Rule-based components for French clinical notes
• Trainable components: NER, Span classification
• Support for multitask deep-learning models withweights 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 likeBRAT,JSON,Parquet,Pandas orSpark

You can install EDS-NLP viapip. We recommend pinning the library version in your projects, or use a strict package manager likePoetry.

pip install edsnlp==0.15.0

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

pip install"edsnlp[ml]==0.15.0"

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.

importedsnlp,edsnlp.pipesasedsnlp=edsnlp.blank("eds")terms=dict(covid=["covid","coronavirus"],)# Split the documents into sentences, this isneeded for negation detectionnlp.add_pipe(eds.sentences())# Matcher componentnlp.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 thedocumentation 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 thededicated 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 thankAssistance Publique – Hôpitaux de Paris,AP-HP Foundation andInria for funding this project.