用随机过程建模生存时间，让预测结果可解释。
Neural Diffusion Processes for Physically Interpretable Survival Prediction
- 将输入映射为扩散过程的物理参数，生成可解释的生存函数。
- 在真实与合成数据上表现接近最先进模型，且无需比例风险假设。
- 适合需要理解风险变化机制的医疗或工业故障预测场景。
我们提出DeepFHT，一种将深度神经网络与随机过程理论中的首次击中时间（FHT）分布结合的生存分析框架。事件发生时间被建模为潜变量扩散过程首次触及吸收边界的时间。神经网络将输入变量映射到选定FHT过程（如带漂移或无漂移的布朗运动）的物理参数，包括初始状态、漂移和扩散率。该方法导出闭式生存函数与风险函数，能捕捉时变风险而不依赖比例风险假设。在合成与真实数据集上，DeepFHT的预测性能达到当前最优水平，同时保持基于物理的可解释参数化，揭示了输入特征与风险之间的关系。该框架融合随机过程理论与深度学习，为复杂系统中的生存现象建模提供了原则性路径。
原文摘要 · Abstract (English)
We introduce DeepFHT, a survival-analysis framework that couples deep neural networks with first hitting time (FHT) distributions from stochastic process theory. Time to event is represented as the first passage of a latent diffusion process to an absorbing boundary. A neural network maps input variables to physically meaningful parameters including initial condition, drift, and diffusion, within a chosen FHT process such as Brownian motion, both with drift and driftless. This yields closed- form survival and hazard functions and captures time-varying risk without assuming proportional- hazards. We compare DeepFHT with Cox regression using synthetic and real-world datasets. The method achieves predictive accuracy on par with the state-of-the-art approach, while maintaining a physics- based interpretable parameterization that elucidates the relation between input features and risk. This combination of stochastic process theory and deep learning provides a principled avenue for modeling survival phenomena in complex systems
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