SurviVAEl: Variational Autoencoders for Clustering Time Series

Abstract

Multi-state models are generalizations of time-to-event models, where individuals progress through discrete states in continuous time. As opposed to classical approaches to survival analysis which include only alive-dead transitions, states can be competing in nature and transient, enabling richer modelling of complex clinical event series. Classical multi-state models, such as the Cox-Markov model, struggle to capture idiosyncratic, non-linear, time dependent, or high-dimensional covariates for which more sophisticated machine learning models are needed. Recently proposed extensions can overcome these limitations, however, they do not allow for uncertainty quantification of the model prediction, and typically have limited interpretability at the individual or population level. Here, we introduce SurviVAEl, a multi-state survival framework based on a VAE architecture, enabling uncertainty quantification and interpretable patient trajectory clustering.