Multicriteria Optimization of Language Models for Heart Failure With Preserved Ejection Fraction Symptom Detection in Spanish Electronic Health Records: Comparative Modeling Study

Transformer-based models can reliably detect HFpEF-related symptoms from Spanish EHRs, even in the presence of class imbalance and substantial linguistic complexity. The results show that combining domain-specific pretraining with long-context modeling architectures and clinically aligned optimization strategies leads to substantial gains in classification performance, particularly in sensitivity. These models not only achieve high accuracy and generalization on unseen patients but also demonstrate robustness in handling the semantic nuances and narrative structure of real-world clinical documentation. These findings support the potential deployment of Transformer-based systems as effective screening tools to prioritize patients at risk for cardiac amyloidosis in Spanish-speaking health care settings.