Getting started

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==0.11.2

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

pip install "edsnlp[ml]==0.11.2"

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, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")  # (1)

terms = dict(
    covid=["covid", "coronavirus"],  # (2)
)

# Sentencizer component, needed for negation detection
nlp.add_pipe(eds.sentences())  # (3)
# Matcher component
nlp.add_pipe(eds.matcher(terms=terms))  # (4)
# 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  # (5)
# Out: (covid,)

doc.ents[0]._.negation  # (6)
# Out: True

1. 'eds' is the name of the language, which defines the tokenizer.
2. This example terminology provides a very simple, and by no means exhaustive, list of synonyms for COVID19.
3. Similarly to spaCy, pipes are added via the nlp.add_pipe method.
4. See the matching tutorial for mode details.
5. spaCy stores extracted entities in the Doc.ents attribute.
6. The eds.negation component has adds a negation custom attribute.

This example is complete, it should run as-is.

Tutorials

To learn more about EDS-NLP, we have prepared a series of tutorials that should cover the main features of the library.

Available pipeline components

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}
}