Summary of Survrelu: Inherently Interpretable Survival Analysis Via Deep Relu Networks, by Xiaotong Sun et al.
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks
by Xiaotong Sun, Peijie Qiu, Shengfan Zhang
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel deep survival model, SurvReLU, that leverages the strengths of both neural networks and tree-based structures. By deliberately constructing a deep rectified linear unit (ReLU) network, SurvReLU combines the representational power of deep survival models with the interpretability of traditional tree-based survival models. The proposed method outperforms previous approaches on both simulated and real-world survival benchmark datasets in terms of performance and interoperability. The authors demonstrate the effectiveness of SurvReLU by applying it to various tasks, including mortality prediction and patient stratification. With its ability to handle censoring and time-to-event distributions, SurvReLU has significant implications for a wide range of applications in medicine, social sciences, and other fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to analyze how long it takes for something to happen, like when someone will die or when a patient will get better. It uses special computer models called neural networks that are very good at learning patterns in data. However, these models can be hard to understand, which is important if we want to make decisions based on the results. This paper combines two different approaches: one that is very good at learning but hard to understand, and another that is easier to understand but not as good at learning. The result is a new model that does well in both areas. The authors tested this model on real-world data and showed that it works better than previous models. |
Keywords
* Artificial intelligence * Relu
