You can deploy this model and start earning money today!

raphael-r/
bright-eds-molecular

Name: bright-eds-molecular
Availability: InStock

Views

No views yet

BRIGHT NER: EDS-NLP (CamemBERT + CRF) fine-tuned for molecular

Description

This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the molecular 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.

Fields

It extracts the following fields (described in French):

mol_idh1: Statut mutation IDH1
mol_idh2: Statut mutation IDH2
mol_mgmt: Méthylation promoteur MGMT
mol_h3f3a: Mutation H3F3A
mol_hist1h3b: Mutation HIST1H3B
mol_tert: Mutation promoteur TERT
mol_CDKN2A: Délétion homozygote CDKN2A
mol_atrx: Mutation ATRX
mol_cic: Mutation CIC
mol_fubp1: Mutation FUBP1
mol_fgfr1: Mutation FGFR1
mol_egfr_mut: Mutation EGFR
mol_prkca: Mutation PRKCA
mol_pten: Mutation PTEN
mol_p53: Mutation p53
mol_braf: Mutation BRAF

Performance on Validation Set

Aggregates:

Macro F1: 0.5636 (Precision: 0.5866, Recall: 0.5486)
Micro F1: 0.8612 (Precision: 0.8680, Recall: 0.8544)

Per-Label Breakdowns:

Label	Precision	Recall	F1
mol_idh1	0.9481	0.9309	0.9394
mol_idh2	0.7929	0.7929	0.7929
mol_mgmt	0.8317	0.8984	0.8638
mol_h3f3a	0.8571	0.9231	0.8889
mol_hist1h3b	0.0000	0.0000	0.0000
mol_tert	0.8283	0.8542	0.8410
mol_CDKN2A	0.8148	0.7333	0.7719
mol_atrx	0.9714	0.7556	0.8500
mol_cic	0.9130	0.7000	0.7925
mol_fubp1	0.8276	0.9600	0.8889
mol_fgfr1	0.0000	0.0000	0.0000
mol_egfr_mut	0.0000	0.0000	0.0000
mol_prkca	0.0000	0.0000	0.0000
mol_pten	0.6000	0.4286	0.5000
mol_p53	0.0000	0.0000	0.0000
mol_braf	1.0000	0.8000	0.8889

Usage

python

# Inference Code
import edsnlp

nlp = edsnlp.load("raphael-r/bright-eds-molecular")
doc = nlp("Patient presenting with epileptic seizures...")

for ent in doc.ents:
    print(ent.text, "=>", ent.label_)