  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 between health
variables as well as clusters of strongly connected health variables. We use
English Longitudinal Study of Aging (ELSA) data to train our model and show
that it performs better than multiple dedicated linear models for health
outcomes and survival. We compare our model with flexible lower-dimensional
latent-space models to explore the dimensionality required to accurately model
aging health outcomes. Our DJIN model can be used to generate synthetic
individuals that age realistically, to impute missing data, and to simulate
future aging outcomes given arbitrary initial health states.
