Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records.

ABSTRACT: Electronic health records (EHRs) from type 2 diabetes (T2D) patients consist of longitudinally and sparsely measured health markers at clinical encounters. Our goal is to use such data to learn latent patterns that can inform patient's health status related to T2D while accounting for challenges in retrospectively collected EHRs. To handle challenges such as correlated longitudinal measurements, irregular and informative encounter times, and mixed marker types, we propose multivariate generalized linear models to learn latent patient subgroups. In our model, covariate effects were time-dependent and latent Gaussian processes were introduced to model between-marker correlations over time. Using inferred latent processes, we integrated the irregularly measured health markers of mixed types i

SUBMITTER: Lou J 

PROVIDER: S-EPMC8033418 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature