Neural Network-Based Piecewise Survival Models

Olov Holmer; Erik Frisk; Mattias Krysander

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arXiv · 2403.18664

Neural Network-Based Piecewise Survival Models

Olov Holmer·Erik Frisk·Mattias Krysander

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Abstract

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.

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stat.ML cs.LG cs.SY eess.SY

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BibTeX RIS

Olov Holmer, Erik Frisk, Mattias Krysander. 2024-03-27. Neural Network-Based Piecewise Survival Models. https://arxiv.org/abs/2403.18664

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