  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.
