Predictive Modelling of Longitudinal Health and Disease Trajectories Using Multi-Modal Population Data

This project aims to develop predictive models of how health states evolve over time within individuals, using the Lifelines cohort. By integrating clinical measurements, questionnaire data, processed genetic risk scores, and registry-linked health outcomes across multiple assessment waves, we will build models that capture how individuals transition between health and disease states. These models will identify early markers of disease onset across four domains: cardiometabolic disease, respiratory disease, musculoskeletal disorders, and mental health conditions. All derived data products and analytical methods will be returned to Lifelines.