  Conventional survival analysis methods are typically ineffective to
characterize heterogeneity in the population while such information can be used
to assist predictive modeling. In this study, we propose a hybrid survival
analysis method, referred to as deep clustering survival machines, that
combines the discriminative and generative mechanisms. Similar to the mixture
models, we assume that the timing information of survival data is generatively
described by a mixture of certain numbers of parametric distributions, i.e.,
expert distributions. We learn weights of the expert distributions for
individual instances according to their features discriminatively such that
each instance's survival information can be characterized by a weighted
combination of the learned constant expert distributions. This method also
facilitates interpretable subgrouping/clustering of all instances according to
their associated expert distributions. Extensive experiments on both real and
synthetic datasets have demonstrated that the method is capable of obtaining
promising clustering results and competitive time-to-event predicting
performance.
