The increasing prevalence of ‘‘multimodal’’ data sources—such as survey data, biomarker concentrations, anthropometric measures, and clinical diagnoses—offers a potential route for improvement, but simple Cox models are not well suited to these complex data points.

Recent research has started to develop a framework that selects a subset of candidate predictors for a coronary artery disease (CAD) risk prediction tool from a multimodal space of 13,782 features using elastic net regularized Cox regression. The approach selected 51 of 13,782 candidate predictors, and the resulting model demonstrated improved prediction of incident CAD compared with clinically used algorithms among a set of participants.