HERBERT-P is a contrastive-learning-based bi-encoder for medical entity normalization in Spanish, leveraging synonym and parent relationships from UMLS to enhance candidate retrieval for entity linking in clinical texts.

Key features:

Base model: PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
Trained with 15 positive pairs per anchor (synonyms + parents)
Task: Normalization of disease, procedure, and symptom mentions to SNOMED-CT/UMLS codes.
Domain: Spanish biomedical/clinical texts.
Corpora: DisTEMIST, MedProcNER, SympTEMIST.

Benchmark Results

Corpus	Top-1	Top-5	Top-25	Top-200
DisTEMIST	0.574	0.720	0.803	0.869
SympTEMIST	0.630	0.779	0.881	0.945
MedProcNER	0.651	0.763	0.838	0.892

ICB-UMA/HERBERT-P

HERBERT: Leveraging UMLS Hierarchical Knowledge to Enhance Clinical Entity Normalization in Spanish

Benchmark Results

ICB-UMA/
HERBERT-P