Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring.

ABSTRACT: A two-level Gaussian process (GP) joint model is proposed to improve personalized prediction of medical monitoring data. The proposed model is applied to jointly analyze multiple longitudinal biomedical outcomes, including continuous measurements and binary outcomes, to achieve better prediction in disease progression. At the population level of the hierarchy, two independent GPs are used to capture the nonlinear trends in both the continuous biomedical marker and the binary outcome, respectively; at the individual level, a third GP, which is shared by the longitudinal measurement model and the longitudinal binary model, induces the correlation between these two model components and strengthens information borrowing across individuals. The proposed model is particularly advantageous in per

SUBMITTER: Duan LL 

PROVIDER: S-EPMC5868980 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature