Neural Network-Based Piecewise Survival Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911816835137536 |
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| author | Holmer, Olov Frisk, Erik Krysander, Mattias |
| author_facet | Holmer, Olov Frisk, Erik Krysander, Mattias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network-Based Piecewise Survival Models Holmer, Olov Frisk, Erik Krysander, Mattias Machine Learning Systems and Control 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. |
| title | Neural Network-Based Piecewise Survival Models |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2403.18664 |