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.
翻译:在医学中,生存分析研究死亡等有关事件的时间期限。一个重大挑战是如何处理多种相互竞争的事件(如多重疾病诊断)。在这项工作中,我们提出了一个基于变压器的模式,该模式不假定基本生存分布,能够处理相互竞争的事件,即SurvTRACE。我们在多活动情况下的观察环境中对隐含的隐含作用作出了说明,这导致选择偏差,因为预测生存概率受不相关因素的影响。为了充分利用生存数据从零开始培训变压器,设计了多重辅助任务,用于多任务学习。因此,该模型从所有这些任务中学习了强有力的共同代表,进而有助于更好的生存分析。我们进一步展示了如何通过SurvTRACE的可解释关注机制来检查共变的相关性和重要性。 SurvTRACE的观察机制足以在加强临床试验设计和新的治疗发展方面有很大潜力。对METABRIC、支助和SERR数据的实验,有470k病人验证了我们方法的全近优势。