README.md

---
license: apache-2.0
language:
- es
base_model:
- PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
tags:
- medical
- spanish
- bi-encoder
- entity-linking
- sapbert
- umls
- snomed-ct
---

# **ClinLinker**

## Model Description

ClinLinker is a state-of-the-art bi-encoder model for medical entity linking (MEL) in Spanish, optimized for clinical domain tasks. It enriches concept representations by incorporating synonyms from the UMLS and SNOMED-CT ontologies. The model was trained with a contrastive-learning strategy using hard negative mining and multi-similarity loss.

## 💡 Intended Use
- **Domain**: Spanish Clinical NLP
- **Tasks**: Entity linking (diseases, symptoms, procedures) to SNOMED-CT
- **Evaluated On**: DisTEMIST, MedProcNER, SympTEMIST
- **Users**: Researchers and practitioners working in clinical NLP

## 📈 Performance Summary (Top-25 Accuracy)

| Model               | DisTEMIST | MedProcNER | SympTEMIST |
|--------------------|-----------|------------|------------|
| **ClinLinker**     | **0.845** | **0.898**  | **0.909**  |
| ClinLinker-KB-P    | 0.853     | 0.891      | 0.918      |
| ClinLinker-KB-GP   | 0.864     | 0.901      | 0.922      |
| SapBERT-XLM-R-large| 0.800     | 0.850      | 0.827      |
| RoBERTa biomedical | 0.600     | 0.668      | 0.609      |

*Results correspond to the cleaned gold-standard version (no "NO CODE" or "COMPOSITE").*

## 🧪 Usage

```python
from transformers import AutoModel, AutoTokenizer
import torch

model = AutoModel.from_pretrained("ICB-UMA/ClinLinker")
tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/ClinLinker")

mention = "insuficiencia renal aguda"
inputs = tokenizer(mention, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
embedding = outputs.last_hidden_state[:, 0, :]
print(embedding.shape)
```

For scalable retrieval, use [Faiss](https://github.com/facebookresearch/faiss) or the [`FaissEncoder`](https://github.com/ICB-UMA/KnowledgeGraph) class.

## ⚠️ Limitations
- The model is optimized for Spanish clinical data and may underperform outside this domain.
- Expert validation is advised in critical applications.

## 📚 Citation

> Gallego, F., López-García, G., Gasco-Sánchez, L., Krallinger, M., Veredas, F.J. (2024). ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_19

## Authors

Fernando Gallego, Guillermo López-García, Luis Gasco-Sánchez, Martin Krallinger, Francisco J Veredas