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--- |
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license: apache-2.0 |
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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-clinical-es |
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tags: |
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- medical |
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- spanish |
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- bi-encoder |
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- entity-linking |
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- sapbert |
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- umls |
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- snomed-ct |
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--- |
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# **ClinLinker** |
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## Model Description |
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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. |
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## 💡 Intended Use |
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- **Domain**: Spanish Clinical NLP |
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- **Tasks**: Entity linking (diseases, symptoms, procedures) to SNOMED-CT |
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- **Evaluated On**: DisTEMIST, MedProcNER, SympTEMIST |
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- **Users**: Researchers and practitioners working in clinical NLP |
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## 📈 Performance Summary (Top-25 Accuracy) |
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| Model | DisTEMIST | MedProcNER | SympTEMIST | |
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|--------------------|-----------|------------|------------| |
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| **ClinLinker** | **0.845** | **0.898** | **0.909** | |
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| ClinLinker-KB-P | 0.853 | 0.891 | 0.918 | |
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| ClinLinker-KB-GP | 0.864 | 0.901 | 0.922 | |
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| SapBERT-XLM-R-large| 0.800 | 0.850 | 0.827 | |
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| RoBERTa biomedical | 0.600 | 0.668 | 0.609 | |
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*Results correspond to the cleaned gold-standard version (no "NO CODE" or "COMPOSITE").* |
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## 🧪 Usage |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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import torch |
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model = AutoModel.from_pretrained("ICB-UMA/ClinLinker") |
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tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/ClinLinker") |
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mention = "insuficiencia renal aguda" |
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inputs = tokenizer(mention, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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embedding = outputs.last_hidden_state[:, 0, :] |
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print(embedding.shape) |
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``` |
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For scalable retrieval, use [Faiss](https://github.com/facebookresearch/faiss) or the [`FaissEncoder`](https://github.com/ICB-UMA/KnowledgeGraph) class. |
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## ⚠️ Limitations |
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- The model is optimized for Spanish clinical data and may underperform outside this domain. |
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- Expert validation is advised in critical applications. |
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## 📚 Citation |
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> 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 |
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## Authors |
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Fernando Gallego, Guillermo López-García, Luis Gasco-Sánchez, Martin Krallinger, Francisco J Veredas |
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