Survival cluster analysis

Published

Journal Article

© 2020 ACM. Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions. An approach that addresses this need is likely to improve the characterization of individual outcomes by leveraging regularities in subpopulations, thus accounting for population-level heterogeneity. In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles. Experiments on real-world datasets show consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.

Full Text

Duke Authors

Cited Authors

  • Chapfuwa, P; Li, C; Mehta, N; Carin, L; Henao, R

Published Date

  • February 4, 2020

Published In

  • Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning

Start / End Page

  • 60 - 68

Digital Object Identifier (DOI)

  • 10.1145/3368555.3384465

Citation Source

  • Scopus