Overview
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.
| 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 |