Inferring Multidimensional Rates of Aging from Cross-Sectional Data

• Published in: Proceedings of machine learning research Vol. 89; pp. 97 - 107
• Main Authors: Pierson, Emma, Koh, Pang Wei, Hashimoto, Tatsunori, Koller, Daphne, Leskovec, Jure, Eriksson, Nicholas, Liang, Percy
• Format: Journal Article
• Language: English
• Published: United States 01.04.2019
• ISSN: 2640-3498

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.