  In the following short article we adapt a new and popular machine learning
model for inference on medical data sets. Our method is based on the
Variational AutoEncoder (VAE) framework that we adapt to survival analysis on
small data sets with missing values. In our model, the true health status
appears as a set of latent variables that affects the observed covariates and
the survival chances. We show that this flexible model allows insightful
decision-making using a predicted distribution and outperforms a classic
survival analysis model.
