  There has been increasing interest in modelling survival data using deep
learning methods in medical research. Current approaches have focused on
designing special cost functions to handle censored survival data. We propose a
very different method with two steps. In the first step, we transform each
subject's survival time into a series of jackknife pseudo conditional survival
probabilities and then use these pseudo probabilities as a quantitative
response variable in the deep neural network model. By using the pseudo values,
we reduce a complex survival analysis to a standard regression problem, which
greatly simplifies the neural network construction. Our two-step approach is
simple, yet very flexible in making risk predictions for survival data, which
is very appealing from the practice point of view. The source code is freely
available at http://github.com/lilizhaoUM/DNNSurv.
