Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:97-107, 2019.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-pierson19a, title = {Inferring Multidimensional Rates of Aging from Cross-Sectional Data}, author = {Pierson, Emma and Koh, Pang Wei and Hashimoto, Tatsunori and Koller, Daphne and Leskovec, Jure and Eriksson, Nick and Liang, Percy}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {97--107}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/pierson19a/pierson19a.pdf}, url = {https://proceedings.mlr.press/v89/pierson19a.html}, 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.} }
Endnote
%0 Conference Paper %T Inferring Multidimensional Rates of Aging from Cross-Sectional Data %A Emma Pierson %A Pang Wei Koh %A Tatsunori Hashimoto %A Daphne Koller %A Jure Leskovec %A Nick Eriksson %A Percy Liang %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-pierson19a %I PMLR %P 97--107 %U https://proceedings.mlr.press/v89/pierson19a.html %V 89 %X 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.
APA
Pierson, E., Koh, P.W., Hashimoto, T., Koller, D., Leskovec, J., Eriksson, N. & Liang, P.. (2019). Inferring Multidimensional Rates of Aging from Cross-Sectional Data. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:97-107 Available from https://proceedings.mlr.press/v89/pierson19a.html.

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