Summary of Neural Network-based Piecewise Survival Models, by Olov Holmer et al.
Neural Network-Based Piecewise Survival Models
by Olov Holmer, Erik Frisk, Mattias Krysander
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a family of neural network-based survival models that extend traditional discrete-time and piecewise exponential models. The proposed models use piecewise definitions of the hazard function and density function on a partitioning of time, resulting in four different models with varying levels of complexity. The authors demonstrate the effectiveness of these models using a simulated dataset, showing they perform similarly to state-of-the-art energy-based models but require significantly less computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops new survival models that can help predict when things will happen or stop happening. The researchers created a set of neural network-based models that are more flexible than usual methods. They tested these models using fake data and found they worked well, almost as good as the best current approach, but were much faster to compute. |
Keywords
* Artificial intelligence * Neural network
