Abstract
The rapid accumulation of biomedical cohort data presents opportunities to explore disease mechanisms, risk factors, and prognostic markers. However, current research often has a narrow focus, limiting the exploration of risk factors and inter-disease correlations. Additionally, fragmented processes and time constraints can hinder comprehensive analysis of the disease landscape. Our work addresses these challenges by integrating multimodal data from the UK Biobank, including basic, lifestyle, measurement, environment, genetic, and imaging data. We propose UKB-MDRMF, a comprehensive framework for predicting and assessing health risks across 1560 diseases. Unlike single disease models, UKB-MDRMF incorporates multimorbidity mechanisms, resulting in superior predictive accuracy, with all disease types showing improved performance in risk assessment. By jointly predicting and assessing multiple diseases, UKB-MDRMF uncovers shared and distinctive connections among risk factors and diseases, offering a broader perspective on health and multimorbidity mechanisms.