In medicine, survival analysis studies the time duration to events of interest such as mortality. One major challenge is how to deal with multiple competing events (e.g., multiple disease diagnoses). In this work, we propose a transformer-based model that does not make the assumption for the underlying survival distribution and is capable of handling competing events, namely SurvTRACE. We account for the implicit \emph{confounders} in the observational setting in multi-events scenarios, which causes selection bias as the predicted survival probability is influenced by irrelevant factors. To sufficiently utilize the survival data to train transformers from scratch, multiple auxiliary tasks are designed for multi-task learning. The model hence learns a strong shared representation from all these tasks and in turn serves for better survival analysis. We further demonstrate how to inspect the covariate relevance and importance through interpretable attention mechanisms of SurvTRACE, which suffices to great potential in enhancing clinical trial design and new treatment development. Experiments on METABRIC, SUPPORT, and SEER data with 470k patients validate the all-around superiority of our method.

本文介绍了一种基于Transformer的SurvTRACE模型用于处理医学中处理多个竞争事件的生存分析，并提出了多个辅助任务以更好地利用生存数据训练Transformer，以及如何通过SurvTRACE的可解释注意机制检查协变量相关性和重要性，从而使临床试验设计和新治疗方法的开发更具潜力。该方法在METABRIC、SUPPORT和SEER数据上进行的实验证明了其全面的优越性。

SurvTRACE: 基于Transformer的竞争风险生存分析