  There has been increasing interest in modeling survival data using deep
learning methods in medical research. In this paper, we proposed a Bayesian
hierarchical deep neural networks model for modeling and prediction of survival
data. Compared with previously studied methods, the new proposal can provide
not only point estimate of survival probability but also quantification of the
corresponding uncertainty, which can be of crucial importance in predictive
modeling and subsequent decision making. The favorable statistical properties
of point and uncertainty estimates were demonstrated by simulation studies and
real data analysis. The Python code implementing the proposed approach was
provided.
