Learning the shared structure of human health across diseases, modalities, and time

Human disease risk emerges from the shared influences of genetics, environment, lifestyle, and concurrent diseases over time, resulting in recurring patterns of susceptibility across conditions. However, most risk prediction models treat diseases as independent outcomes or rely on limited input variables, restricting their ability to capture these shared patterns. Here we present RisQ, a framework that learns a unified representation of human health across diseases, modalities, and time. This representation is queried with natural language to estimate disease risk for arbitrary diseases and prediction horizons. Generalization to unseen disease groups and prediction horizons indicates that information is shared across diseases and time, revealing a common structure of disease risk that is learnable. Trained and validated in 488,170 participants from the UK Biobank and evaluated without retraining in 257,538 participants from the independent All of Us cohort, RisQ leverages this shared structure to outperform disease-specific models, multi-disease frameworks, and tabular foundation models in risk prediction. We show that jointly modeling increasing numbers of diseases, input modalities, and prediction horizons improves performance, indicating that scaling these axes increases information transfer and enriches the learned structure. We then show this structure is multi-scale: it captures demographic determinants of disease susceptibility, while also organizing individuals into reproducible cross-disease risk clusters within demographically restricted subgroups. Genetic analyses further support the biological grounding of the structure by linking gene-level loss of function to cross-disease risk profiles. This surfaces known relationships of HBB , SLC22A12 , CASR , and LDLR , while also highlighting less characterized associations. Together, these results indicate that human disease risk exhibits a shared structure that can be learned from multimodal data to improve risk prediction, stratify individuals by cross-disease susceptibility, and support the discovery of relationships across diseases.