Deep learning for the dynamic prediction of multivariate longitudinal and survival data.

ABSTRACT: The joint model for longitudinal and survival data improves time-to-event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the longitudinal and survival submodels. In addition, computational difficulties arise when considering multiple longitudinal outcomes due to the large number of random effects to be integrated out in the full likelihood. In this article, we discuss several recent machine learning methods for incorporating multivariate longitudinal data for time-to-event prediction. The presented methods use functional data analysis or convolutional neural networks to model the longitudinal data, both of which scale well to multiple longitudinal outcomes. In ad

SUBMITTER: Lin J 

PROVIDER: S-EPMC9232978 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature