Aladynoulli: Hierarchical Bayesian Modeling of time-varying trajectories across 358 distinct diseases

Music City Center 

This proposal introduces a novel Bayesian framework for modeling disease progression that accounts for the complex temporal relationships between cardiovascular disease and its comorbidities. The model leverages latent disease signatures that evolve over time, allowing diseases to manifest through different pathways and with varying progression rates. By incorporating time-varying signatures through Gaussian processes, the framework captures how disease patterns emerge and evolve across the lifespan, while a discrete-time survival likelihood enables prediction of disease trajectories. The model integrates genetic effects through individual-specific signature loadings, allowing for personalized progression rates. The framework addresses key limitations of existing approaches: it moves beyond assumptions of disease independence, handles the streaming nature of electronic health records through Bayesian updating, and enables joint modeling of heterogeneous disease types across lifespans. Validation on UK Biobank data (N=407,878) across 358 diseases demonstrates identification of 20 biologically meaningful disease signatures. This approach advances statistical methodology for analyzing longitudinal health data while enabling precision medicine applications through improved disease trajectory prediction.

Dynamic factor analysis