Physiological Chronic Disease Risk Stratification to Passive Mental Health Surveillance via Digital Phenotyping

This expansive analytical study explores the intersection of high-performance computing, predictive epidemiology, and behavioral computational linguistics. The primary contribution of this research is a dual-vector health analytics ecosystem. Vector A outlines a supervised learning processing environment designed for early-stage screening of widespread chronic medical conditions, specifically Type-2 Diabetes Mellitus (T2DM) and generalized Cardiovascular Disease (CVD), using complex clinical parameters. Vector B establishes an autonomous, non-invasive digital phenotyping framework that implements advanced Natural Language Processing (NLP) layers to extract semantic emotional indicators from open social communication nodes (such as Reddit and Twitter). The underlying motivation is to bridge the historical gap between physical biomarkers and behavioral expressions. By using state-of-the-art transformer models alongside advanced ensemble architectures, this architecture achieves critical improvements over current benchmark approaches, establishing a reliable path toward continuous, non-invasive digital healthcare infrastructure.