Skip to main content

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

Project description

Tests Documentation PyPI Demo Coverage DOI

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

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==0.13.0

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

pip install "edsnlp[ml]==0.13.0"

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.

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.

@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.

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-0.13.0.tar.gz (1.6 MB view details)

Uploaded Source

Built Distributions

edsnlp-0.13.0-cp312-cp312-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.12 Windows x86-64

edsnlp-0.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

edsnlp-0.13.0-cp312-cp312-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

edsnlp-0.13.0-cp312-cp312-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.12 macOS 10.9+ x86-64

edsnlp-0.13.0-cp311-cp311-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.11 Windows x86-64

edsnlp-0.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

edsnlp-0.13.0-cp311-cp311-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

edsnlp-0.13.0-cp311-cp311-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

edsnlp-0.13.0-cp310-cp310-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.10 Windows x86-64

edsnlp-0.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

edsnlp-0.13.0-cp310-cp310-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

edsnlp-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

edsnlp-0.13.0-cp39-cp39-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

edsnlp-0.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

edsnlp-0.13.0-cp39-cp39-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

edsnlp-0.13.0-cp39-cp39-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

edsnlp-0.13.0-cp38-cp38-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

edsnlp-0.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

edsnlp-0.13.0-cp38-cp38-macosx_11_0_arm64.whl (2.0 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

edsnlp-0.13.0-cp38-cp38-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

edsnlp-0.13.0-cp37-cp37m-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.7m Windows x86-64

edsnlp-0.13.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

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

edsnlp-0.13.0-cp37-cp37m-macosx_10_9_x86_64.whl (2.0 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file edsnlp-0.13.0.tar.gz.

File metadata

  • Download URL: edsnlp-0.13.0.tar.gz
  • Upload date:
  • Size: 1.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0.tar.gz
Algorithm Hash digest
SHA256 d8e0847acb397d2621b00cab10ddc262131e7a1d8f84cac71a25db20bbe6e8c4
MD5 e58571991fc4d4fda7b3834ced496a20
BLAKE2b-256 a837676735a18883a416d804b5a25ef2ec833c573c5859f53b5be82e6e8e3335

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 f645d40f4782f71bcb96b2b38648874adcd7ff171fa0ec05911bcf9aef45ac48
MD5 362c909dedbcc263465a0e4a1db3cab1
BLAKE2b-256 b4737ba68cf45422c4e14ebed1aacaaa294a1a52f9bb220d9fd3a9fa6b7f5878

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8df43a586093377093975e720c41983e0e7bd1f2a4f681d451cd9eff81533604
MD5 a6ad0a5398299473b5efa6adb1f3d24a
BLAKE2b-256 63165c24cb0f1d1fefaf1eb3137f19ca69a356021834fe7a7652055a12ac1550

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 17b3a77bc3d13a4c73958b9d706c216d48bf3834e8cc65869675af06774c3bb2
MD5 ec21ada24e380c695ab4c1e368a59a3a
BLAKE2b-256 8b0e49dcb1679bfb659342eaf5bab0d4d92876e68c477587ba44a6774efcc565

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 32ff22317d87f1703f6ded3a7e3440cf78a229554d5674ccd13cbdf893ba77f4
MD5 8ecf30d33a369fec35ae35c61f599c29
BLAKE2b-256 afefb7028d752db83dcd13926b25ff277a6d1aea3ccfe381992118c2ea22e56d

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 bf14efd0b193c2715578391cda39b9bb5eca4ddd556ead20bdb0c3353ca4caae
MD5 c4d92288940948aae79f5c1ebc9f4cf5
BLAKE2b-256 54485560337d70ef33beafc22dfbafbd435f5fe87f513b617fb8f55605c7e006

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 93179073d8ccbc3c31647789b35418f048a7f46a2aa6d921576c3b22687341d7
MD5 52dc182941d8fc933a708d7d76413076
BLAKE2b-256 2e6efb83b89702aeb8b007d913982f0e23d46abf2ab1e2776c64acc53968ae53

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 974efff286850254e9382ccba831368253c5fed8db7058ccfe5ae2da71f1bbd3
MD5 cb3369daf91bc5ed818f91c72f902497
BLAKE2b-256 6369e421a1e4702d916576438853fd02f5c9e811fc69124bc98beecc0697ed31

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e8ceab406e5d202081d1a3b1cd024494fb17999998bd1ecb70a3a2f73efe17ed
MD5 0f618188e30ad6f42bb58c2ff2e91851
BLAKE2b-256 08fa72564e33732a3bdbf2a9693b82bd4abe6d90fae7da9979665df67815afcc

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 48f1e2dce9cf96d4d7268cb4a123efff64fd369e913cbed6ddd134c7fba7b54c
MD5 188f440134dda82770d8a0ca5fa5095c
BLAKE2b-256 6ce934cf1d6df117e6b83e4f16a18c0309306c109226d1965ea7a45efd0e093f

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ad18d36c05c52c2374451c2d2ba26186b8f3bace253bab45a4353434641f0aee
MD5 fb5ce994a89237ae5d6ff64cff45501c
BLAKE2b-256 5b99c2b821780fb777d4f11e940b9fd5243e8b400b5dd3866fb153474065dd76

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 41d9b0b908dc2908b13a954e9cb2a6b68716ab8940bdc6bc73e749dbdf565511
MD5 dc4963c1f28a6f4bedd64e3ae6b53706
BLAKE2b-256 68e579ddbf9faacb4b568a5ba76954a8ef2c6d8c9c532a339b98c869550652e7

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a0821d7db0f68763a9508101cb6361afb4ae3abcf5bf6927a61099e94c71c2c0
MD5 d239d2cc17bce106107ed9d6d433e5e3
BLAKE2b-256 497a005142b3515b7556bd9adb10fd78337c3ac59575ce3c79371d070ff000cb

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 f37d939fd583a1386cba71dd42da483df24c1f3f7d351b22c5e816d3ab67858d
MD5 eaa0fac4eefc01ef2cba133cdf5dfbb5
BLAKE2b-256 a75ab104c764461d6036fc4a2e603ff0ecede5e73eff6eab5c58f87ce697e5a1

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d45a41ac1c57d9ef44571191e77918c97daac374995d330440a94cd7497afa8c
MD5 6015aa73997adea8377b6336a4522d8f
BLAKE2b-256 95917ff70325d8ffa031bae7cf516ea8e051ce034e1de8eb0b8b33308780ef84

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4efe91602cdc3504abd7a8cebbfdcf1016bb96b99d153eb363a427c7f66c0da5
MD5 0f2b643e3fb6e91632c6c2634e56865d
BLAKE2b-256 82ef48a72308c445adca93d8706f2ac00c834dabcdf478dcadf90a8ea6c0d56a

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 abb8c4eda5f3d82584b9a8d41cb533c48ebbfa3273ad57bc34bef5834202adb3
MD5 d80fcfbff1fc9c333be56489184e0925
BLAKE2b-256 63eb248f1f4386a468329dca4475e90eef53287ab54a2de127d0ccdee681c2eb

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 a53bb9ebea9d026ffbe28fe09925beba910acabf7623360f6f72f65f28f09b97
MD5 5122188339978242202a30eee5a65098
BLAKE2b-256 6c80a59d95f273e0515672902c61fc64438e78c643e592017807666dff833473

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8c60bed840247bb8f69039d26504b4e062fb798ee1118fadf3ad8f3c28459921
MD5 db78fffd1362a219d71fecedac8a32f4
BLAKE2b-256 d44ba686376364e310dd763e6741a9c765c17b81709720a41f230255a58e9417

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9e5044ceb7574fe75578a5dffee6a592dbddaaaad012c36915b22bf0efc16cb4
MD5 4177a93cab94fcb9ddb6d29b39bfe776
BLAKE2b-256 139563bad6c554e53a64ef65f0d54450b1094cb082c3a552f2bcf5fedd8420dc

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9a9503051f04254f2e275c1b52936ac623fb2b56232315ad7120443ffb1589d8
MD5 1833915b36744ae7131ebe99ce813ed8
BLAKE2b-256 2cb53555545b152b151ca9441c7cabb165cb34444040fd0ec5d8184f820c96ee

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: edsnlp-0.13.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for edsnlp-0.13.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 eb45825c78cd929a6f0035f6acfa5956b345989067fd22b3d7d13c20c6d743c3
MD5 3be8b2feabea3b712a5ec20b2d28b52d
BLAKE2b-256 8339db045e937a167d43584f7e6061824be425ad4ad8789e08fa62af978c9371

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b88d4fa943215e886c73b6404c84bfb3d4c2d180cd9b694844ed89ec450e698f
MD5 455bde63544aaf001d34bf1dc996b2b4
BLAKE2b-256 9128e50593c126c6d8c82442aee02b5a22ab8e5892a46db2b688448675a51c93

See more details on using hashes here.

File details

Details for the file edsnlp-0.13.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for edsnlp-0.13.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1662cb8bb943c745ffbcfcf3b85080481c2e9aeb055707b7f197e0e8bd70b271
MD5 8e8b1177270d8e94eec7b93f329e9431
BLAKE2b-256 4f276a10a45cf535f3599e209a9a66601231aaac691b97061b9926f5941115e5

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