Skip to main content

A set of spaCy components to extract information from clinical notes written in French.

Project description

Tests Documentation PyPI Demo Codecov DOI

EDS-NLP

EDS-NLP provides a set of spaCy components that are used to extract information from clinical notes written in French.

Check out the interactive demo!

If it's your first time with spaCy, we recommend you familiarise yourself with some of their key concepts by looking at the "spaCy 101" page in the documentation.

Quick start

Installation

You can install EDS-NLP via pip:

pip install edsnlp

We recommend pinning the library version in your projects, or use a strict package manager like Poetry.

pip install edsnlp==0.6.2

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 spacy

nlp = spacy.blank("fr")

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

# Sentencizer component, needed for negation detection
nlp.add_pipe("eds.sentences")
# Matcher component
nlp.add_pipe("eds.matcher", config=dict(terms=terms))
# Negation detection
nlp.add_pipe("eds.negation")

# Process your text in one call !
doc = nlp("Le patient est atteint de covid")

doc.ents
# Out: (covid,)

doc.ents[0]._.negation
# Out: False

Documentation

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.

@misc{edsnlp,
  author = {Dura, Basile and Wajsburt, Perceval and Petit-Jean, Thomas 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    = {http://aphp.github.io/edsnlp}
}

Acknowledgement

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

edsnlp-0.6.2.tar.gz (1.2 MB view details)

Uploaded Source

Built Distributions

edsnlp-0.6.2-cp310-cp310-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

edsnlp-0.6.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

edsnlp-0.6.2-cp310-cp310-macosx_10_9_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

edsnlp-0.6.2-cp39-cp39-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

edsnlp-0.6.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

edsnlp-0.6.2-cp39-cp39-macosx_10_9_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

edsnlp-0.6.2-cp38-cp38-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.8 Windows x86-64

edsnlp-0.6.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

edsnlp-0.6.2-cp38-cp38-macosx_10_9_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

edsnlp-0.6.2-cp37-cp37m-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.7m Windows x86-64

edsnlp-0.6.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

edsnlp-0.6.2-cp37-cp37m-macosx_10_9_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file edsnlp-0.6.2.tar.gz.

File metadata

  • Download URL: edsnlp-0.6.2.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for edsnlp-0.6.2.tar.gz
Algorithm Hash digest
SHA256 17b32b88d97299468d3c67375bc54192cb7a7cd37b65d6672466cec180be838e
MD5 e9b4897f4d9da750ce45721f56119902
BLAKE2b-256 6e731f986aaf1105d9b99100b72074997a982181e902b9eb9372743544c0c292

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.6.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for edsnlp-0.6.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 15923ba0b173eb868a160cdd39268296ed7b8c1d8f79c24743d3a248b073ff7f
MD5 76fad500e0e57c2777444f3cc4ac9662
BLAKE2b-256 5106500326038b189fb608db102d2567216e40e53c7ada0376d98f236d3d7273

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bc84d2289a701b7ffc8281ea61adc1479a592067a786f53061cb43591b4ddd77
MD5 7af6ff5a1305d69aa0805ba5bbabbfa0
BLAKE2b-256 8832b07fa4f85be6f7b9ee6219b12e04374c59a196ef01ad8cca8c144d60b2cf

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e09d13cc7e926e736b2cf885c66073fc54bda65d44396c3b7ba8ab6185ddc137
MD5 b8b2c5406465947f48e50ae6d686d4c0
BLAKE2b-256 d7dd35c460e9d24797078c6cde0ea63080f23a0bdad3ec303240e60542f2f3ac

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.6.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for edsnlp-0.6.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 79c2c29dbd0b21c3e655c4fa3743c52b91b96816fd4a463845c5a5015d91ac00
MD5 ecc82428e35f62aa0ac28c01a030666e
BLAKE2b-256 2c0138a71fa3f603afaabef5bd90fb5ccf6a123d8e90b759cb4d0acab4ba41b7

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5f0af992c0027cf551f5d7f605dde109ccf73918be61fda42c017d577ae79965
MD5 5167ebe5118620f647bc9dec3010d76b
BLAKE2b-256 c7165fe5b9ea53c692f7cbf7f7423f36a56c9f84f1802298e004deaf202f5a54

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1a6a0ec125a84af9a7a770e45d90f0383f13fba1566ea17a3a20a4d0fd8d280a
MD5 e4b9fccea75ec8c5f0bd7046d6c1e13c
BLAKE2b-256 da23b150ed8f6fcea276a9bae7b661309c2a87163719f279cf93a263a94e0c8b

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.6.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for edsnlp-0.6.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 5cbcb91b9cab2bb426974b9fbbc3ffdf481e31f618a1309351a2fc024c3a3f03
MD5 d257bebd46f80b26a28115b629a40182
BLAKE2b-256 bfe3c7c83e3a739669e5ec5c1686a32e49620b82e6ff46cd43824f73dbf14fae

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 45434d093b4e441d2864f8ddce48e8672ff8b16fc42f47acf919b51a33361199
MD5 9f6fbd93ba28457082370474aed8bedc
BLAKE2b-256 f54a8027aa6b70441d31e9865d75b4c6308285e63046edd2d16ff6bde3c881ee

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9548f3e515c363eceadc394738f73a5a0370385b66aba000fff29a0614f6389e
MD5 528a4395cb9802ee6788d26ffc7a76cc
BLAKE2b-256 9b2eb5e5f07c85d5051d1228cc83a7737a391ce82579e11314649622409dfe0b

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.6.2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for edsnlp-0.6.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 76893567cd8a32fade2f6a4564b792769860f7192e85dce4f696f140d1986bca
MD5 83f2d6bea5c402119947b03586128c8f
BLAKE2b-256 9f2456bd8047ee6f69ba58790bcf0f04046b2e7a6e14d3ce72b149b54291c084

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e951b3604fbe30e2815a52b40bc27a095405b6a39e2a23637494441796d9e697
MD5 30481c892d56108fa4741e8daf632331
BLAKE2b-256 47011edcfcb7ff0ece5a1e8b69ace68f3df3bdab7da246d433f6ad72afe3d380

See more details on using hashes here.

File details

Details for the file edsnlp-0.6.2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.6.2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 10a33f2b38907035e80d5ed6f9a4ed35aa8b01e33bc635bb99c74402adc4a697
MD5 1dd08f63e842e305d810a5ad6e79a5d8
BLAKE2b-256 3bc0bd9544b1bfb0c889ef0f032578e610403ab57109a5a949ffb4f1885e8deb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page