SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks

TL;DR

SurvReLU introduces an inherently interpretable deep ReLU network for survival analysis that combines the interpretability of tree-based models with the performance of neural networks, validated on benchmark datasets.

Contribution

This paper presents SurvReLU, a novel deep ReLU network architecture that achieves interpretability in survival analysis while maintaining high performance.

Findings

Effective on simulated datasets

Competitive results on real benchmarks

Balances interpretability and accuracy

Abstract

Survival analysis models time-to-event distributions with censorship. Recently, deep survival models using neural networks have dominated due to their representational power and state-of-the-art performance. However, their "black-box" nature hinders interpretability, which is crucial in real-world applications. In contrast, "white-box" tree-based survival models offer better interpretability but struggle to converge to global optima due to greedy expansion. In this paper, we bridge the gap between previous deep survival models and traditional tree-based survival models through deep rectified linear unit (ReLU) networks. We show that a deliberately constructed deep ReLU network (SurvReLU) can harness the interpretability of tree-based structures with the representational power of deep survival models. Empirical studies on both simulated and real survival benchmark datasets show the effectiveness of the proposed SurvReLU in terms of performance and interoperability. The code is available at \href{https://github.com/xs018/SurvReLU}{\color{magenta}{ https://github.com/xs018/SurvReLU}}.