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arXiv · 2305.11575

The Deep Promotion Time Cure Model

Victor Medina-Olivares·Stefan Lessmann·Nadja Klein
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Abstract

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.

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Victor Medina-Olivares, Stefan Lessmann, Nadja Klein. 2023-05-19. The Deep Promotion Time Cure Model. https://doi.org/10.1109/tnnls.2024.3398559

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