Survival analysis is a statistical method employed to scrutinize the duration until a specific event of interest transpires, known as time-to-event information characterized by censorship. Recently, deep learning-based methods have dominated this field due to their representational capacity and state-of-the-art performance. However, the black-box nature of the deep neural network hinders its interpretability, which is desired in real-world survival applications but has been largely neglected by previous works. In contrast, conventional tree-based methods are advantageous with respect to interpretability, while consistently grappling with an inability to approximate the global optima due to greedy expansion. In this paper, we leverage the strengths of both neural networks and tree-based methods, capitalizing on their ability to approximate intricate functions while maintaining interpretability. To this end, we propose a Neural Survival Oblique Tree (NSOTree) for survival analysis. Specifically, the NSOTree was derived from the ReLU network and can be easily incorporated into existing survival models in a plug-and-play fashion. Evaluations on both simulated and real survival datasets demonstrated the effectiveness of the proposed method in terms of performance and interpretability.

翻译：生存分析是一种统计方法，用于研究特定感兴趣事件发生前的时间长度，即具有删失特征的事件发生时间信息。近年来，基于深度学习的方法因其表征能力和最先进的性能而主导了这一领域。然而，深度神经网络的黑箱性质阻碍了其可解释性——这在现实生存应用中至关重要，却被以往工作所忽视。相比之下，传统基于树的方法在可解释性方面具有优势，但始终因贪婪扩展而难以逼近全局最优解。本文利用神经网络和基于树的方法的优势，既能近似复杂函数，又能保持可解释性。为此，我们提出了一种用于生存分析的神经生存斜向树（NSOTree）。具体而言，NSOTree源自ReLU网络，可以即插即用方式轻松集成到现有生存模型中。在模拟和真实生存数据集上的评估证明了该方法在性能和可解释性方面的有效性。