  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.

Machine Learning;Artificial Intelligence;Machine Learning