  Survival Analysis (SA) constitutes the default method for time-to-event
modeling due to its ability to estimate event probabilities of sparsely
occurring events over time. In this work, we show how to improve the training
and inference of SA models by decoupling their full expression into (1) an
aggregated baseline hazard, which captures the overall behavior of a given
population, and (2) independently distributed survival scores, which model
idiosyncratic probabilistic dynamics of its given members, in a fully
parametric setting. The proposed inference method is shown to dynamically
handle right-censored observation horizons, and to achieve competitive
performance when compared to other state-of-the-art methods in a variety of
real-world datasets, including computationally inefficient Deep Learning-based
SA methods and models that require MCMC for inference. Nevertheless, our method
achieves robust results from the outset, while not being subjected to
fine-tuning or hyperparameter optimization.

Machine Learning;Machine Learning