Joint Bayesian Modelling of Internal Dependencies and Relevant Multimorbidities of a Heterogeneous Disease

Abstract

A heterogeneous target disease represented by multiple descriptors and disease subtypes frequently has a rich internal dependency structure. The identification of comorbidities and particularly the multimorbidities of such diseases requires very large sample size as relevant comorbidities may form complex interactions. We demonstrate this phenomena by applying a Bayesian probabilistic graphical model on a large-scale medical datasets UK Biobank (117,392 samples), specifically by showing that in this case the posterior landscape of multimorbidities is still flat. As a potential solution, we evaluate a Bayesian method, which provides a hierarchic, multivariate characterization of strongly relevant morbidities and a Bayesian, systems-based score for exploring interactions for a heterogeneous disease. It explores complete sets of strongly relevant comorbidities using full multivariate representation for the internal dependencies within the target disease. We used depression as target, a heterogeneous disease in the UK Biobank dataset. Results are compared against scenarios using a univariate and an independent, multivariate representation of the target medical condition, specifically investigating multitarget interaction posteriors and its approximations.