A neural-Bayesian approach to survival analysis

Standard survival analysis can be given a neural interpretation in terms of a multilayer perceptron (MLP) with exponential transfer functions. More hidden units accommodate more complex relationships. The neural interpretation suggests a Bayesian analysis, which allows one to introduce sensible priors and to sample from the posterior. We also propose a method for computing p-values from the obtained ensemble of networks, since this is the kind of information medical experts are familiar with. We apply our methods on a database regarding patients with ovarian cancer