RESUMO
We propose a novel method for predicting time-to-event data in the presence of cure fractions based on flexible survival models integrated into a deep neural network (DNN) framework. Our approach allows for nonlinear relationships and high-dimensional interactions between covariates and survival and is suitable for large-scale applications. To ensure the identifiability of the overall predictor formed of an additive decomposition of interpretable linear and nonlinear effects and potential higher-dimensional interactions captured through a DNN, we employ an orthogonalization layer. We demonstrate the usefulness and computational efficiency of our method via simulations and apply it to a large portfolio of U.S. mortgage loans. Here, we find not only a better predictive performance of our framework but also a more realistic picture of covariate effects.