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--- |
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library_name: transformers |
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tags: |
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- contrastive-learning |
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- Spanish-UMLS |
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- Hierarchical-enrichment |
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license: mit |
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language: |
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- es |
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base_model: |
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- PlanTL-GOB-ES/roberta-base-biomedical-es |
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--- |
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# HERBERT: Leveraging UMLS Hierarchical Knowledge to Enhance Clinical Entity Normalization in Spanish |
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**HERBERT-GP** is a contrastive-learning-based bi-encoder for medical entity normalization in Spanish. |
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It leverages hierarchical relationships from UMLS (parents and grandparents) to enhance the candidate retrieval step for entity linking in Spanish clinical texts. |
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**Key features:** |
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- Base model: [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) |
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- Trained with 15 positive pairs per anchor using synonyms, parents, and grandparents from UMLS/SNOMED-CT. |
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- Task: Normalization of disease, procedure, and symptom mentions to SNOMED-CT/UMLS codes. |
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- Domain: Spanish biomedical/clinical texts. |
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- Corpora: DisTEMIST, MedProcNER, SympTEMIST. |
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--- |
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## Evaluation (top-k accuracy): |
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| Corpus | Top-1 | Top-5 | Top-25 | Top-200 | |
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|-------------|--------|--------|--------|---------| |
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| DisTEMIST | 0.574 | 0.720 | 0.803 | 0.871 | |
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| SympTEMIST | 0.630 | 0.779 | 0.886 | 0.949 | |
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| MedProcNER | 0.655 | 0.767 | 0.840 | 0.894 | |
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