Neural Network-Based Piecewise Survival Models

• URL: http://arxiv.org/abs/2403.18664v1
• Date: Wed, 27 Mar 2024 15:08:00 GMT
• Title: Neural Network-Based Piecewise Survival Models
• Authors: Olov Holmer, Erik Frisk, Mattias Krysander,
• Abstract summary: A family of neural network-based survival models is presented. The models can be seen as an extension of the commonly used discrete-time and piecewise exponential models.
• Score: 0.3999851878220878
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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