TransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure

arXiv:2404.03804v1 Announce Type: cross
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 …

abstract accounting applications arxiv biomedical cs.lg data death dependencies epidemiology event events modeling social social sciences stat.ap stat.me stat.ml studies survival terminal type