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==0.7.1

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-0.7.1.tar.gz (1.3 MB view details)

Uploaded Source

Built Distributions

edsnlp-0.7.1-cp310-cp310-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.10 Windows x86-64

edsnlp-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

edsnlp-0.7.1-cp310-cp310-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

edsnlp-0.7.1-cp39-cp39-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.9 Windows x86-64

edsnlp-0.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

edsnlp-0.7.1-cp39-cp39-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

edsnlp-0.7.1-cp38-cp38-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.8 Windows x86-64

edsnlp-0.7.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

edsnlp-0.7.1-cp38-cp38-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

edsnlp-0.7.1-cp37-cp37m-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.7m Windows x86-64

edsnlp-0.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB view details)

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

edsnlp-0.7.1-cp37-cp37m-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file edsnlp-0.7.1.tar.gz.

File metadata

  • Download URL: edsnlp-0.7.1.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for edsnlp-0.7.1.tar.gz
Algorithm Hash digest
SHA256 c6584edf20468d0242e0c3a6a0e801c1274b4ad0ea4de098584d124dc5e03945
MD5 2c7a16e6de3f991b5edee06bebb45fda
BLAKE2b-256 d7bf34b72a55f70fbb281808cecbf46f46dc449befd73f1aa8714a4b27a3d3ef

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.7.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for edsnlp-0.7.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 42067cfacfc9b5ce6e5eb7bd7da787031a593f615016dd6baa9f153ffa196782
MD5 c8046bf94e23fe10fc66bedce9d1d539
BLAKE2b-256 df079e7067c7dfa95ebf0df2770b4e3249083e4e80e276e9e731b47429f1feb3

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7ab6147165a9d367fb44bbd1e0e264f3e6a2b7abb3c8d73a6acaf2f82a97857e
MD5 9e900e7ae8b63db72cd134b7da15b9e1
BLAKE2b-256 8078eeb4cbdbf707f9f647cfc2ac9c039a22a02f893180c20b13c09ccb24483a

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ae0be066406767c886a3e52316bc0bda3da81d98a09396e4b12f0918a3b277ed
MD5 2eb0350757a84f76f6e1a357d6efaed5
BLAKE2b-256 fa26c5680f887f68bf5165500d8e1bd0ecdb1d767a2979479dcfb1eb23973ab1

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.7.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for edsnlp-0.7.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5426ede5001a5264d4352cc6a3222cc3db4ba0bb773722fa947bf5b1749ee178
MD5 b67b0d9c014478e822d26141cb1c8c2b
BLAKE2b-256 73703ffa6bbf61b285398e164f0342903ba5f655707eef7b47430d710fc12719

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 85479431f780f6befce2bec125773b6693aa727e68da02b3c17d10e8c5712f75
MD5 40dfbeb91fa7afe905b5ca9165039f14
BLAKE2b-256 45b9f3fef06165e393cf80936deb9c3c3fead8c174a5e3dd431b625554aa20f5

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3db23ea5ebe41bcddcbef3c8034821230f1101ce2aaaee1340324468f838acf0
MD5 951278839df016a1c4d07baf18f229bf
BLAKE2b-256 e5b7dcf5105ff376a5f0a1e93cfdc44a90914f7eeb86871760751d328b18a0a4

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.7.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for edsnlp-0.7.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 f716e90bff43ddeebe2200801739091d99af8e9ea8a9c29a18b4ddcc149a2b54
MD5 1bd45873d361fd2ce0b4fed545c5cb39
BLAKE2b-256 0f800a39281946880d9fedd20f9e8752304d39de927266a104efb6565d320b0a

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1bbd0b703e8b4d4d8708937e8cfcbe8fac5124170cc269251d83939cc4a7956a
MD5 241d62007b76fb181115dae3f38b4bdf
BLAKE2b-256 2a42eaefb866c921657d0e9fa3a8e4992e8c8f6285d323de63d0fcf9abb656c7

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7297f998fc634fbe7fa00278bad3ddd728628112ab59d0c98f264fd94d5adae9
MD5 89d4e4e086ccdb92094ce6f0af568d8c
BLAKE2b-256 6b7768b8f299aab9aa2092998e42f30326fec4adee788b3d081314481a8ff3cd

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.7.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.14

File hashes

Hashes for edsnlp-0.7.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 16368e319b46f02a2a32990bad825feeaafc3a94a9714a77974281062dcb086a
MD5 ba3276e55489df286714ac42f1ba2cd6
BLAKE2b-256 7cf477af13aaebb43dc098076e61a98d052e09915c412ef2fca2339a73edbd90

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cc2caec92c7c552232e9a041a274015b73530a07b1bc56b09c10629872c69111
MD5 240b8b2407c1d17c892f1d5b7a57a942
BLAKE2b-256 5e3361cb8b585986c3d65e2d9cc5c076616e5169d9b3a67c2421f590e87553bd

See more details on using hashes here.

Provenance

File details

Details for the file edsnlp-0.7.1-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.7.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1baff929618339769ed54b23dab7958308b899228c32d76a8415d4753131a22f
MD5 d3d13e697e387d4f84e0ccac1b9004dc
BLAKE2b-256 a5a88abde2674bc0fb0b22379b015759a1e5d0bec5560f04095f7fdd70ac9688

See more details on using hashes here.

Provenance

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