  Survival analysis/time-to-event models are extremely useful as they can help
companies predict when a customer will buy a product, churn or default on a
loan, and therefore help them improve their ROI. In this paper, we introduce a
new method to calculate survival functions using the Multi-Task Logistic
Regression (MTLR) model as its base and a deep learning architecture as its
core. Based on the Concordance index (C-index) and Brier score, this method
outperforms the MTLR in all the experiments disclosed in this paper as well as
the Cox Proportional Hazard (CoxPH) model when nonlinear dependencies are
found.
