Deep-CSA: Deep Contrastive Learning for Dynamic Survival Analysis With Competing Risks

Survival analysis (SA) is widely used to analyze data in which the time until the event is of interest. Conventional SA techniques assume a specific form for viewing the distribution of survival time as the hitting time of a stochastic process, and explicitly model the relationship between covariates and the distribution of the events hitting time. Although valuable, existing SA models seldom consider to model the dynamic correlations between covariates and more than one event of interest (i.e., competing risks) in the disease progression of subjects. To alleviate this critical problem, we propose a novel deep contrastive learning model to obtain a deep understanding of disease progression of subjects with competing risks from their longitudinal observational data. Specifically, we design a self-supervised objective for learning dynamic representations of subjects suffering from multiple competing risks, such that the relationship between covariates and each specific competing risk changes over time can be well captured. Experiments on two open-source clinical datasets, i.e., MIMIC-III and EICU, demonstrate the effectiveness of our proposed model, with remarkable improvements over the state-of-the-art SA models.