We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our approach allows for non-linear relationships and high-dimensional interactions between covariates and survival and is suitable for large-scale applications. Furthermore, we allow the method to incorporate an identified predictor formed of an additive decomposition of interpretable linear and non-linear effects and add an orthogonalization layer to capture potential higher dimensional interactions. We demonstrate the usefulness and computational efficiency of our method via simulations and apply it to a large portfolio of US mortgage loans. Here, we find not only a better predictive performance of our framework but also a more realistic picture of covariate effects.

翻译：我们提出了一种基于灵活生存模型集成到深度神经网络框架中的新方法，用于在存在治愈比例的情况下预测事件发生时间。该方法允许协变量与生存之间存在非线性关系和高维交互，适用于大规模应用场景。此外，我们允许该方法纳入由可解释线性与非线性效应的加性分解构成的标识预测因子，并通过添加正交化层来捕获潜在的高维交互。通过数值模拟验证了该方法的有效性和计算效率，并将其应用于大型美国抵押贷款组合。在此应用中，我们不仅发现该框架具有更优的预测性能，还揭示了协变量效应更符合实际的图景。