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

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.

A quantile frailty index without dichotomization.

measures of health quantify the aging process of individuals. They should be interpretable, associated with future adverse outcomes, and straightforward to assemble. We use the rank-ordering of risk within a population to construct a quantile frailty index (QFI) that avoids dichotomization, is convenient and interpretable, and is associated with adverse outcomes.

The potential for complex computational models of aging.

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality.

Generating synthetic aging trajectories with a weighted network model using cross-sectional data.

We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health.