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    <abstract>This paper describes the system submitted by our team to the MultiClinAI shared task at the 11th SMM4H-HeaRD Workshop (ACL 2026). The task addresses multilingual clinical Named Entity Recognition (NER) for three entity types (Disease, Procedure, and Symptom) in Spanish clinical texts. Our approach fine-tunes MrBERT-biomed, a domain-adapted ModernBERT model pre-trained on biomedical corpora, using multilingual clinical data from seven European languages. We train independent entity-specific models, each optimized via Bayesian hyperparameter search with Optuna, and apply a deterministic post-processing step that aligns predicted spans to word boundaries. On the official test set, our system achieves overall strict micro-F1 scores of 0.7453, 0.7107, and 0.6603 for Disease, Procedure, and Symptom, respectively.</abstract>
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%0 Conference Proceedings
%T SINAI at #SMM4H–HeaRD 2026: Multilingual Clinical NER with MrBERT-biomed and Optuna Hyperparameter Optimization
%A Piñar, Lucas Molino
%A Diaz-Galiano, Manuel Carlos
%A Martín-Valdivia, María-Teresa
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F pinar-etal-2026-sinai
%X This paper describes the system submitted by our team to the MultiClinAI shared task at the 11th SMM4H-HeaRD Workshop (ACL 2026). The task addresses multilingual clinical Named Entity Recognition (NER) for three entity types (Disease, Procedure, and Symptom) in Spanish clinical texts. Our approach fine-tunes MrBERT-biomed, a domain-adapted ModernBERT model pre-trained on biomedical corpora, using multilingual clinical data from seven European languages. We train independent entity-specific models, each optimized via Bayesian hyperparameter search with Optuna, and apply a deterministic post-processing step that aligns predicted spans to word boundaries. On the official test set, our system achieves overall strict micro-F1 scores of 0.7453, 0.7107, and 0.6603 for Disease, Procedure, and Symptom, respectively.
%U https://aclanthology.org/2026.smm4h-1.4/
%P 18-22

Markdown (Informal)

[SINAI at #SMM4H–HeaRD 2026: Multilingual Clinical NER with MrBERT-biomed and Optuna Hyperparameter Optimization](https://aclanthology.org/2026.smm4h-1.4/) (Piñar et al., SMM4H 2026)

• SINAI at #SMM4H–HeaRD 2026: Multilingual Clinical NER with MrBERT-biomed and Optuna Hyperparameter Optimization (Piñar et al., SMM4H 2026)

ACL

• Lucas Molino Piñar, Manuel Carlos Diaz-Galiano, and María-Teresa Martín-Valdivia. 2026. SINAI at #SMM4H–HeaRD 2026: Multilingual Clinical NER with MrBERT-biomed and Optuna Hyperparameter Optimization. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 18–22, San Diego, United States. Association for Computational Linguistics.