Overview

EDS-Pseudonymisation is a spaCy-based project used at APHP to extract and replace identifying entities in medical documents.

Getting started

EDS-Pseudonymisation is a spaCy project. We created a single workflow that:

• Converts the datasets to spaCy format
• Trains the pipeline
• Evaluates the pipeline using the test set
• Packages the resulting model to make it pip-installable

To use it, you will need to supply:

• A labelled dataset
• A HuggingFace transformers model, or use camembert-base

In any case, you will need to modify the configuration to reflect these changes.

Entities

Label	Description
ADRESSE	Street address, eg 33 boulevard de Picpus
DATE	Any absolute date other than a birthdate
DATE_NAISSANCE	Birthdate
HOPITAL	Hospital name, eg Hôpital Rothschild
IPP	Internal AP-HP identifier for patients, displayed as a number
MAIL	Email address
NDA	Internal AP-HP identifier for visits, displayed as a number
NOM	Any last name (patients, doctors, third parties)
PRENOM	Any first name (patients, doctors, etc)
SECU	Social security number
TEL	Any phone number
VILLE	Any city
ZIP	Any zip code

Commands

Command	Description
convert	Convert the data to spaCy's binary format
train	Train the NER model
evaluate	Evaluate the model and export metrics
package	Package the trained model as a pip package
visualize-model	Visualize the model's output interactively using Streamlit

Run the command with

spacy project run [command] [options]