Interpretable machine learning for high-dimensional trajectories of aging health. (arXiv:2105.03410v2 [q-bio.QM] UPDATED)

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 …

aging arxiv bio health learning machine machine learning