**Hybrid Transformer‑Bayesian Framework for Micro‑Sleep‑Based Cardiovascular Risk Prediction from Lifelog Data**
Dev.to AI
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NLP
Data Science
1. Introduction Cardiovascular disease (CVD) remains the leading cause of death worldwide. Early detection of asymptomatic risk factors is critical for preventive interventions. Conventional risk scores (e.g., Framingham, ASCVD) use static risk factors and fail to capture dynamic, physiological changes driven by lifestyle and sleep patterns. Recently, lifelog data captured by smartphones, wearables, and ambient sensors offer granular, temporally resolved behavioral signals...