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

翻译：我们提出了DeepFHT，一种将深度神经网络与随机过程理论中的首次命中时间分布相结合的生存分析框架。事件发生时间被建模为潜在扩散过程首次到达吸收边界的时间。神经网络将输入变量映射到具有物理意义的参数，包括初始条件、漂移项和扩散系数，这些参数内置于选定的首次命中时间过程（如带漂移与无漂移的布朗运动）中。该方法可导出闭式生存函数与风险函数，且无需假设比例风险即可捕捉时变风险。我们通过合成数据集与真实数据集将DeepFHT与Cox回归模型进行对比。该方法在预测准确性上达到与前沿方法相当的水平，同时保持了基于物理的可解释参数化框架，能够阐明输入特征与风险之间的内在关联。随机过程理论与深度学习的结合为复杂系统中的生存现象建模提供了理论严谨的新途径。