TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.

ABSTRACT: In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events, and survival data while accounting for their dependencies is critical. While joint models for the three components exist in statistical literature, many of these approaches are limited by heavy parametric assumptions and scalability issues. Recently, incorporating deep learning techniques into joint modeling has shown promising results. However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events. In this paper, we develop TransformerLS

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11757036 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature