  With the rapid advances of deep learning, many computational methods have
been developed to analyze nonlinear and complex right censored data via deep
learning approaches. However, the majority of the methods focus on predicting
survival function or hazard function rather than predicting a single valued
time to an event. In this paper, we propose a novel method, DeepCENT, to
directly predict the individual time to an event. It utilizes the deep learning
framework with an innovative loss function that combines the mean square error
and the concordance index. Most importantly, DeepCENT can handle competing
risks, where one type of event precludes the other types of events from being
observed. The validity and advantage of DeepCENT were evaluated using
simulation studies and illustrated with three publicly available cancer data
sets.
