Survival models are used in various fields, such as the development of cancer
treatment protocols. Although many statistical and machine learning models have
been proposed to achieve accurate survival predictions, little attention has
been paid to obtain well-calibrated uncertainty estimates associated with each
prediction. The currently popular models are opaque and untrustworthy in that
they often express high confidence even on those test cases that are not
similar to the training samples, and even when their predictions are wrong. We
propose a Bayesian framework for survival models that not only gives more
accurate survival predictions but also quantifies the survival uncertainty
better. Our approach is a novel combination of variational inference for
uncertainty estimation, neural multi-task logistic regression for estimating
nonlinear and time-varying risk models, and an additional sparsity-inducing
prior to work with high dimensional data.

本文提出一个基于贝叶斯框架、变分推断及神经多任务逻辑回归的生存模型，不但提供更准确的预测，而且能更好地量化生存不确定性。