Survival analysis is a widely known method for predicting the likelihood of an event over time. The challenge of dealing with censored samples still remains. Traditional methods, such as the Cox Proportional Hazards (CPH) model, hinge on the limitations due to the strong assumptions of proportional hazards and the predetermined relationships between covariates. The rise of models based on deep neural networks (DNNs) has demonstrated enhanced effectiveness in survival analysis. This research introduces the Implicit Continuous-Time Survival Function (ICTSurF), built on a continuous-time survival model, and constructs survival distribution through implicit representation. As a result, our method is capable of accepting inputs in continuous-time space and producing survival probabilities in continuous-time space, independent of neural network architecture. Comparative assessments with existing methods underscore the high competitiveness of our proposed approach. Our implementation of ICTSurF is available at https://github.com/44REAM/ICTSurF.

翻译：生存分析是一种广泛用于预测事件随时间发生可能性的方法。处理删失样本的挑战依然存在。传统方法，如Cox比例风险（CPH）模型，受限于比例风险的强假设以及协变量之间预定义关系的局限性。基于深度神经网络（DNNs）的模型的兴起，已在生存分析中展现出更高的有效性。本研究引入了隐式连续时间生存函数（ICTSurF），它建立在连续时间生存模型之上，并通过隐式表示构建生存分布。因此，我们的方法能够接受连续时间空间的输入，并产生连续时间空间的生存概率，且独立于神经网络架构。与现有方法的比较评估突显了我们所提方法的高度竞争力。我们的ICTSurF实现可在 https://github.com/44REAM/ICTSurF 获取。