edsnlp

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

https://github.com/aphp/edsnlp

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 1 DOI reference(s) in README
✓
Academic publication links
Links to: zenodo.org
✓
Committers with academic emails
3 of 30 committers (10.0%) from academic institutions
○
Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (15.3%) to scientific vocabulary

Keywords

clinical-data-warehouse deep-learning fast french medical multi-task nlp pytorch rule-based spacy text-mining

Keywords from Contributors

interactive packaging network-simulation hacking observability agents embedded optim standardization projection

Last synced: 6 months ago · JSON representation ·

Repository

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

Basic Info

Host: GitHub
Owner: aphp
License: bsd-3-clause
Language: Python
Default Branch: master
Homepage: https://aphp.github.io/edsnlp/
Size: 132 MB

Statistics

Stars: 125
Watchers: 9
Forks: 33
Open Issues: 57
Releases: 46

Topics

clinical-data-warehouse deep-learning fast french medical multi-task nlp pytorch rule-based spacy text-mining

Created almost 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Citation

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 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 !

Features

Rule-based components for French clinical notes
Trainable components: NER, Span classification
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

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.

shell pip install edsnlp==0.18.0

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

shell pip install "edsnlp[ml]==0.18.0"

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 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 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, AP-HP Foundation and Inria for funding this project.

Owner

Name: Greater Paris University Hospitals (AP-HP)
Login: aphp
Kind: organization
Location: Paris

Website: https://www.aphp.fr/
Repositories: 35
Profile: https://github.com/aphp

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  EDS-NLP: efficient information extraction from
  French clinical notes
message: If you use EDS-NLP, please cite us as below.
type: software
authors:
  - given-names: Perceval
    family-names: Wajsburt
    affiliation: Assistance Publique – Hôpitaux de Paris
  - given-names: Thomas
    family-names: Petit-Jean
    affiliation: Assistance Publique – Hôpitaux de Paris
  - given-names: Basile
    family-names: Dura
    orcid: "https://orcid.org/0000-0002-8315-4050"
    affiliation: Assistance Publique – Hôpitaux de Paris
  - given-names: Ariel
    family-names: Cohen
    orcid: "https://orcid.org/0000-0002-2550-9773"
    affiliation: Assistance Publique – Hôpitaux de Paris
  - given-names: Charline
    family-names: Jean
    affiliation: Assistance Publique – Hôpitaux de Paris
  - given-names: Romain
    family-names: Bey
    affiliation: Assistance Publique – Hôpitaux de Paris
repository-code: "https://github.com/aphp/edsnlp"
url: "http://aphp.github.io/edsnlp"
abstract: >-
  EDS-NLP provides a set of spaCy components that are
  used to extract information from clinical notes
  written in French.
keywords:
  - NLP
  - clinical
license: BSD-3-Clause
year: 2022
doi: 10.5281/zenodo.6424993

GitHub Events

Total

Create event: 56
Release event: 7
Issues event: 40
Watch event: 10
Delete event: 97
Member event: 2
Issue comment event: 509
Push event: 575
Pull request event: 103
Pull request review event: 37
Pull request review comment event: 41
Fork event: 2

Last Year

Create event: 56
Release event: 7
Issues event: 40
Watch event: 10
Delete event: 97
Member event: 2
Issue comment event: 509
Push event: 575
Pull request event: 103
Pull request review event: 37
Pull request review comment event: 41
Fork event: 2

Committers

Last synced: 9 months ago

All Time

Total Commits: 1,404
Total Committers: 30
Avg Commits per committer: 46.8
Development Distribution Score (DDS): 0.622

Past Year

Commits: 202
Committers: 12
Avg Commits per committer: 16.833
Development Distribution Score (DDS): 0.144

Top Committers

Name	Email	Commits
Perceval Wajsbürt	p**t@a**r	531
Basile Dura	b**t@a**r	386
Basile Dura	b**a@p**u	153
Thomas PETIT-JEAN	t**t@a**r	126
Ariel Cohen	a**t@a**r	72
Perceval Wajsburt	“**t@s**”	32
Adam	a**i@a**r	24
Charline Jean	c**t@a**r	13
etienneguevel	e**l@a**r	12
Keyvan	k**m@g**m	8
Lucas Dedieu	4****u	8
Julien Duquesne	j**e@h**r	6
svittoz	s**t@a**r	6
Keyvan BEROUKHIM	k**m@a**r	6
sebastien cossin	s**n@u**r	3
Théo Jolivet	t**t@e**r	2
Yannis Cattan	5****n	2
clementjumel	c**l@g**m	2
Clément Vinot	4****t	1
François Konschelle	3****t	1
mko	m**t@a**r	1
keyber	k**m@e**r	1
VITTOZ Simon	s**d@a**r	1
Matthieu	m**t@g**m	1
SamirAk	4****S	1
Théo Ryffel	t**r@g**m	1
Vincent M	m**t@y**r	1
dependabot[bot]	4****]	1
isabelbt	1****t	1
paul-bssr	7****r	1

Committer Domains (Top 20 + Academic)

aphp.fr: 11 etu.upmc.fr: 1 ens-paris-saclay.fr: 1 u-bordeaux.fr: 1 sorbonne-universite.fr”: 1 polytechnique.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 83
Total pull requests: 315
Average time to close issues: 2 months
Average time to close pull requests: 17 days
Total issue authors: 25
Total pull request authors: 24
Average comments per issue: 0.89
Average comments per pull request: 1.38
Merged pull requests: 261
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 33
Pull requests: 110
Average time to close issues: 10 days
Average time to close pull requests: 6 days
Issue authors: 12
Pull request authors: 7
Average comments per issue: 0.36
Average comments per pull request: 2.05
Merged pull requests: 85
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

percevalw (24)
aricohen93 (11)
LucasDedieu (7)
Thomzoy (4)
Aremaki (4)
cvinot (4)
marieverdoux (3)
julienduquesne (2)
0dey (2)
louni-g (2)
JudithCo (2)
paul-bssr (2)
theoimbert-aphp (2)
Eliseliuaphp (2)
bdura (2)

Pull Request Authors

percevalw (202)
aricohen93 (22)
Thomzoy (21)
LucasDedieu (15)
svittoz (12)
etienneguevel (7)
Aremaki (5)
julienduquesne (5)
ycattan (3)
TheooJ (3)
bdura (3)
isabelbt (2)
pidoux7 (2)
cvinot (2)
LaRiffle (2)

Top Labels

Issue Labels

enhancement (5) bug (4) discussion (1)

Pull Request Labels

enhancement (1) dependencies (1)

Packages

Total packages: 2
Total downloads:
- pypi 5,520 last-month

Total dependent packages: 1
(may contain duplicates)
Total dependent repositories: 1
(may contain duplicates)
Total versions: 94
Total maintainers: 3

proxy.golang.org: github.com/aphp/edsnlp

Documentation: https://pkg.go.dev/github.com/aphp/edsnlp#section-documentation
License: bsd-3-clause
Latest release: v0.17.2
published 8 months ago

Versions: 48
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 6.5%

Average: 6.7%

Dependent repos count: 6.9%

Last synced: 6 months ago

pypi.org: edsnlp

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

Documentation: https://aphp.github.io/edsnlp
License: Copyright 2021 Assistance Publique - Hôpitaux de Paris Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Latest release: 0.18.0
published 6 months ago

Versions: 46
Dependent Packages: 1
Dependent Repositories: 1
Downloads: 5,520 Last month

Rankings

Downloads: 5.8%

Dependent packages count: 7.3%

Stargazers count: 7.4%

Forks count: 8.3%

Average: 10.2%

Dependent repos count: 22.1%

Maintainers (3)

ThomasPJ percevalw RomainBeyAPHP

Last synced: 6 months ago

Dependencies

.github/workflows/documentation.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/release.yml actions

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actions/upload-artifact v2 composite
pypa/cibuildwheel v2.4.0 composite
pypa/gh-action-pypi-publish v1.5.0 composite

.github/workflows/test-build.yml actions

actions/checkout v2 composite
pypa/cibuildwheel v2.4.0 composite

.github/workflows/tests.yml actions

actions/cache v3 composite
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actions/checkout v2 composite
actions/setup-java v2 composite
actions/setup-python v3 composite
actions/setup-python v2 composite
codecov/codecov-action v2 composite
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demo/requirements.txt pypi

streamlit *

pyproject.toml pypi

decorator *
loguru *
numpy >=1.15.0,<1.23.2; python_version<'3.8'
numpy >=1.15.0; python_version>='3.8'
pandas >=1.1.0,<2.0.0; python_version<'3.8'
pandas >=1.4.0,<2.0.0; python_version>='3.8'
pendulum >=2.1.2
pydantic >=1.10.2,<2.0.0
pysimstring >=1.2.1
regex *
rich >=12.0.0
scikit-learn >=1.0.0
spacy >=3.1,<4.0.0
thinc >=8.1.10
tqdm *
typing_extensions >=4.0.0; python_version<'3.8'
typing_extensions <4.6.0,>=4.0.0; python_version>='3.8'
umls-downloader >=0.1.1

setup.py pypi

edsnlp

Science Score: 77.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

EDS-NLP

Features

Quick start

Installation

A first pipeline

Split the documents into sentences, this isneeded for negation detection

Matcher component

Negation detection (we also support spacy-like API !)

Process your text in one call !

Out: (covid,)

Out: True

Documentation & Tutorials

Disclaimer

Contributing to EDS-NLP

Citation

Acknowledgement

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/aphp/edsnlp

Rankings

pypi.org: edsnlp

Rankings

Maintainers (3)

Dependencies