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EDS-NLP

=======

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](https://spacy.io) to represent documents and their annotations, and [Pytorch](https://pytorch.org/) 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](https://aphp.github.io/edsnlp/demo/) !

## Features

- [Rule-based components](https://aphp.github.io/edsnlp/latest/pipes/) for French clinical notes

- [Trainable components](https://aphp.github.io/edsnlp/latest/pipes/trainable): NER, Span classification

- Support for multitask deep-learning models with [weights sharing](https://aphp.github.io/edsnlp/latest/concepts/torch-component/#sharing-subcomponents)

- [Fast inference](https://aphp.github.io/edsnlp/latest/concepts/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](https://aphp.github.io/edsnlp/latest/data/standoff/), [JSON](https://aphp.github.io/edsnlp/latest/data/json/), [Parquet](https://aphp.github.io/edsnlp/latest/data/parquet/), [Pandas](https://aphp.github.io/edsnlp/latest/data/pandas/) or [Spark](https://aphp.github.io/edsnlp/latest/data/spark/)

## 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](https://python-poetry.org/).

```shell

pip install edsnlp==0.17.0

```

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

```shell

pip install "edsnlp[ml]==0.17.0"

```

### 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.

```python

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

```

## Documentation & Tutorials

Go to the [documentation](https://aphp.github.io/edsnlp) 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](https://aphp.github.io/edsnlp/latest/contributing/) for detail.

## Citation

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

```bibtex

@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](https://www.aphp.fr/), [AP-HP Foundation](https://fondationrechercheaphp.fr/) and [Inria](https://www.inria.fr) for funding this project.