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

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 or the FaissEncoder 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