Nationwide Multimodal Artificial Intelligence Framework for Early Prediction of Chronic Disease Progression Using Electronic Health Records and Social Determinants of Health

Abstract

Chronic disease progression remains a major challenge for national healthcare systems because risk often develops gradually across clinical,
behavioral, environmental, and socioeconomic dimensions. Existing prediction models frequently rely on limited electronic health record
variables and may overlook unstructured clinical notes, longitudinal patient trajectories, physiologic signals, and social determinants of health
that influence disease worsening. This paper proposes a nationwide multimodal artificial intelligence framework for early prediction of chronic
disease progression by integrating structured EHR data, clinical narratives, laboratory histories, medication records, physiologic indicators,
and SDOH variables. The framework combines deep learning, transformer-based EHR modeling, natural language processing, multimodal
fusion, explainable AI, and fairness auditing to support early risk identification and patient stratification across diverse healthcare settings. It
also emphasizes model validation across demographic, geographic, and socioeconomic groups to reduce algorithmic bias and improve clinical
reliability. The proposed framework offers a scalable pathway for preventive intervention, chronic care planning, population health surveillance,
and equitable clinical decision support. By combining medical and social risk signals, the study contributes to a more comprehensive and nationally
deployable approach for predicting chronic disease progression before severe complications occur.