Neural Network-Based Piecewise Survival Models
Olov Holmer Erik Frisk Mattias Krysander
In this paper, a family of neural network-based survival models is presented.
The models are specified based on piecewise definitions of the hazard function
and the density function on a partitioning of the time; both constant and
linear piecewise definitions are presented, resulting in a family of four
models. The models can be seen as an extension of the commonly used
discrete-time and piecewise exponential models and thereby add flexibility to
this set of standard models. Using a simulated dataset the models are shown to
perform well compared to the highly expressive, state-of-the-art energy-based
model, while only requiring a fraction of the computation time.