Exploring the potential of XAI methods in generating clinically meaningful explanations for glycemia prediction in diabetes patients

Abstract Purpose Glycemic emergencies are a frequent cause of hospital admissions and can lead to severe complications, particularly in older or medically complex patients. Anticipating these events is essential for timely intervention and personalized care. This study aimed to identify patients at...

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Autors principals:	Sayna Rotbei, Pablo Matías Soler, Beatriz Merino-Barbancho, Laura Lopez-Perez, Arturo Corbatón Anchuelo, Luis Picazo García, Ricardo Mesanza Forés, Laura Mariel Matus, Ricardo Muñoz Albert, Aitor Odiaga Andicoechea, Raquel Piñero Panadero, María Ángeles San Martín Díez, Ainhoa Burzaco Sánchez, Rosana Soriano Barrón, Andrea Irimia, Esther Ruescas Esculano, Mireia Cramp Vinceixo, F. Beddar Chaib, Hania Tourab, Giuseppe Fico, Alessio Botta
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-03-01
Col·lecció:	BMC Medical Informatics and Decision Making
Matèries:	Diabetes Artificial Intelligence Machine Learning Hyperglycemia Hypoglycemia eXplainable Artificial Intelligence
Accés en línia:	https://doi.org/10.1186/s12911-026-03420-5
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