medical-mt5-clinical-el-spanish
A generative model for
clinical entity linking (ICD-10-CM/ES coding) in Spanish, fine-tuned from
HiTZ/Medical-mT5-large on Spanish clinical text corpora.
Given a clinical mention extracted from a medical document, the model generates the corresponding ICD-10 code in a sequence-to-sequence fashion.
Model Description
| Attribute | Details |
|---|
| Architecture | mT5-large (encoder-decoder) |
| Base model | HiTZ/Medical-mT5-large |
| Task | Clinical entity linking / ICD-10 coding |
| Language | Spanish (es) |
| Parameters | ~1.2B |
| Precision | float32 |
| License | CC BY-NC 4.0 |
This model is part of a comparative study on generative approaches to clinical entity linking in Spanish.
It is one of several fine-tuned variants explored in the associated paper.
Intended Use
Primary Use
- Automated ICD-10 code assignment from Spanish clinical text mentions
- Clinical NLP pipelines for Spanish electronic health records (EHRs)
- Research on generative entity linking in low-resource medical settings
Out-of-Scope Use
- Non-clinical or general-domain text
- Languages other than Spanish
- Direct clinical decision-making without human expert oversight
Training Data
The model was fine-tuned on three Spanish clinical NLP datasets:
| Dataset | Description |
|---|
| Biomedical-TeMU/CodiEsp_corpus | CodiEsp shared task corpus — Spanish clinical cases annotated with ICD-10 codes (diagnoses and procedures) |
| DrBenchmark/E3C | European clinical corpus with entity annotations across multiple languages, including Spanish |
| IIC/CT-EBM-SP | Spanish clinical trial corpus annotated for biomedical entities |
| Mantra GSC | Medline abstract titles, drug labels, biomedical patent claims in Spanish |
Evaluation
Performance is reported as mean ± standard deviation across three runs with different random seeds on the CodiEsp corpus.
| Split | Model | PLI | PLII |
|---|
| TEST | Medical-mT5-C+M | 70.46 ± 0.32 | 77.04 ± 0.35 |
| TEST UNSEEN | Medical-mT5-C+M | 42.46 ± 1.46 | 53.49 ± 1.34 |
The TEST UNSEEN split contains entity mentions whose surface forms were not seen during training, providing a stricter evaluation of generalization.
C refers to the context, which means that the training data was prepared as a concatenation of the entity mention and the sentence where it occurs.
+M refers to Mapped corpus, which were created from the corpora annotated with UMLS CUIs but was mapped to ICD-10 automatically with
ClinIDMap
PLI (Pipeline I) — the model's generated text is compared against all ICD-10 code descriptions in the knowledge base using normalized string matching (diacritics removed, lowercased). A code is assigned when an exact match is found.
PLII (Pipeline II) — extends Pipeline I with semantic text similarity (STS). The generated text and all ICD-10 descriptions are embedded using multilingual SapBERT; the nearest neighbour in the vector space (L2 distance) determines the predicted code. This handles cases where the generated output is semantically close to the correct description but not an exact string match.
How to Use
1from transformers import AutoConfig, MT5ForConditionalGeneration, AutoTokenizer
2
3model_name = "ezotova/medical-mt5-clinical-el-spanish"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5
6config = AutoConfig.from_pretrained(model_name)
7model = MT5ForConditionalGeneration.from_pretrained(model_name, config=config)
8
9# Example: entity mention from a clinical note
10def make_prompt(term, sentence):
11 return f"Genera una definición para el término: {term} - en la frase: {sentence}"
12
13term = "TC abdominal"
14sentence = "La TC abdominal es normal."
15
16prompt = make_prompt(term, sentence)
17
18inputs = tokenizer(prompt, return_tensors="pt", padding=True)
19
20outputs = model.generate(
21 **inputs,
22 max_new_tokens=128,
23 num_beams=5,
24 early_stopping=True
25)
26
27prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
28print(prediction)
29
30# Expected: tomografía computarizada (scanner tc) de abdomen [Code: BW20]
31
Citation
1@article{zotova2025clinical,
2 title = {Generative Models for Clinical Entity Linking in Spanish},
3 author = {Zotova, Elena and Cuadros, Montse and Rigau, German},
4 journal = {Preprint submitted to Elsevier: Available at SSRN 5976036},
5 year = {2026},
6 doi = {[DOI]}
7}
Ethical Considerations
- This model is intended for research purposes only and should not be used as a standalone clinical coding tool without expert review.
- Training data consists of de-identified or synthetic clinical text; however, users should exercise caution when applying the model to real patient data.
- Performance may vary across clinical specialties, document types, and ICD-10 coding guidelines versions.
Acknowledgements
This work was carried out at
Vicomtech in collaboration with the
HiTZ Center (UPV/EHU).
The base model
HiTZ/Medical-mT5-large was developed at HiTZ/IXA and is gratefully acknowledged.
