Interpretable machine learning for high-dimensional trajectories of aging health.

ABSTRACT: We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections betwee

SUBMITTER: Farrell S 

PROVIDER: S-EPMC8782527 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature