| Title: | Inferring Multidimensional Rates of Aging from Cross-Sectional Data. |
| Journal: | Proceedings of machine learning research |
| Published: | 1 Apr 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31538144/ |
| Citations: | 15 (3 in last 2 years) as of 8 Aug 2024 |
Publication 5237
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Abstract
Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional 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 order-isomorphic, 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.</p>
7 Authors
- Emma Pierson
- Pang Wei Koh
- Tatsunori Hashimoto
- Daphne Koller
- Jure Leskovec
- Nicholas Eriksson
- Percy Liang
Enabling scientific discoveries that improve human health