# Neural Network-Based Piecewise Survival Models

arXiv: https://arxiv.org/abs/2403.18664 · HTML: https://mlsanity.com/paper/2403.18664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks](https://mlsanity.com/paper/2008.08637/) (2020)
- [Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis](https://mlsanity.com/paper/2002.09358/) (2020)
- [Energy-Based Survival Models for Predictive Maintenance](https://mlsanity.com/paper/2302.00629/) (2023)

## Abstract (arXiv)

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.