bright_db
Collection
Collection of models for the BRIGHT clinical database project. • 20 items • Updated
This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the dates_outcomes semantic group. It was trained on a synthetic dataset generated for the properly de-identified BRIGHT project dataset (see the generated_data folder in the primary repository).
This model repository was specifically designed to fit within the bright_db overarching namespace.
It extracts the following fields (described in French):
Aggregates:
Per-Label Breakdowns:
| Label | Precision | Recall | F1 |
|---|---|---|---|
| date_chir | 0.1429 | 0.2500 | 0.1818 |
| date_rcp | 0.9211 | 0.8974 | 0.9091 |
| dn_date | 0.0000 | 0.0000 | 0.0000 |
| date_deces | 0.0000 | 0.0000 | 0.0000 |
| date_1er_symptome | 0.0000 | 0.0000 | 0.0000 |
| exam_radio_date_decouverte | 0.1667 | 0.3333 | 0.2222 |
| date_progression | 0.0000 | 0.0000 | 0.0000 |
| survie_globale | 1.0000 | 0.6250 | 0.7692 |
| infos_deces | 0.8000 | 0.8000 | 0.8000 |
# Inference Code
import edsnlp
nlp = edsnlp.load("raphael-r/bright-eds-dates_outcomes")
doc = nlp("Patient presenting with epileptic seizures...")
for ent in doc.ents:
print(ent.text, "=>", ent.label_)