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EDS-Pseudo

The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's Clinical Data Warehouse (EDS).

The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe train.py.

We also provide some fictitious templates (templates.txt) and a script to generate a synthetic dataset generate_dataset.py.

The entities that are detected are listed below.

Downloading the public pre-trained model

The public pretrained model is available on the HuggingFace model hub at AP-HP/eds-pseudo-public and was trained on synthetic data (see generate_dataset.py). You can also test it directly on the demo.

1. Install the latest version of edsnlp

   pip install "edsnlp[ml]" -U

2. Get access to the model at AP-HP/eds-pseudo-public

3. Create and copy a huggingface token https://huggingface.co/settings/tokens?new_token=true

4. Register the token (only once) on your machine

   import huggingface_hub
   
   huggingface_hub.login(token=YOUR_TOKEN, new_session=False, add_to_git_credential=True)

5. Load the model

   import edsnlp
   
   nlp = edsnlp.load("AP-HP/eds-pseudo-public", auto_update=True)
   doc = nlp(
      "En 2015, M. Charles-François-Bienvenu "
      "Myriel était évêque de Digne. C’était un vieillard "
      "d’environ soixante-quinze ans ; il occupait le "
      "siège de Digne depuis 2006."
   )
   
   for ent in doc.ents:
      print(ent, ent.label_, str(ent._.date))

To apply the model on many documents using one or more GPUs, refer to the documentation of edsnlp.

Installation to reproduce

If you'd like to reproduce eds-pseudo's training or contribute to its development, you should first clone it:

git clone https://github.com/aphp/eds-pseudo.git
cd eds-pseudo

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

poetry install

How to use without machine learning

import edsnlp

nlp = edsnlp.blank("eds")

# Some text cleaning
nlp.add_pipe("eds.normalizer")

# Various simple rules
nlp.add_pipe(
    "eds_pseudo.simple_rules",
    config={"pattern_keys": ["TEL", "MAIL", "SECU", "PERSON"]},
)

# Address detection
nlp.add_pipe("eds_pseudo.addresses")

# Date detection
nlp.add_pipe("eds_pseudo.dates")

# Contextual rules (requires a dict of info about the patient)
nlp.add_pipe("eds_pseudo.context")

# Apply it to a text
doc = nlp(
    "En 2015, M. Charles-François-Bienvenu "
    "Myriel était évêque de Digne. C’était un vieillard "
    "d’environ soixante-quinze ans ; il occupait le "
    "siège de Digne depuis 2006."
)

for ent in doc.ents:
    print(ent, ent.label_)

# 2015 DATE
# Charles-François-Bienvenu NOM
# Myriel PRENOM
# 2006 DATE

How to train

Before training a model, you should update the configs/config.cfg and pyproject.toml files to fit your needs.

Put your data in the data/dataset folder (or edit the paths configs/config.cfg file to point to data/gen_dataset/train.jsonl).

Then, run the training script

python scripts/train.py --config configs/config.cfg --seed 43

This will train a model and save it in artifacts/model-last. You can evaluate it on the test set (defaults to data/dataset/test.jsonl) with:

python scripts/evaluate.py --config configs/config.cfg

To package it, run:

python scripts/package.py

This will create a dist/eds-pseudo-aphp-***.whl file that you can install with pip install dist/eds-pseudo-aphp-***.

You can use it in your code:

import edsnlp

# Either from the model path directly
nlp = edsnlp.load("artifacts/model-last")

# Or from the wheel file
import eds_pseudo_aphp

nlp = eds_pseudo_aphp.load()

Documentation

Visit the documentation for more information!

Publication

Please find our publication at the following link: https://doi.org/mkfv.

If you use EDS-Pseudo, please cite us as below:

@article{eds_pseudo,
  title={Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse},
  author={Tannier, Xavier and Wajsb{\"u}rt, Perceval and Calliger, Alice and Dura, Basile and Mouchet, Alexandre and Hilka, Martin and Bey, Romain},
  journal={Methods of Information in Medicine},
  year={2024},
  publisher={Georg Thieme Verlag KG}
}

Acknowledgement

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