Deep learning-based survival analysis with copula-based activation functions for multivariate response prediction

Jong-Min Kim, Il Do Ha and Sangjin Kim ()
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Jong-Min Kim: University of Minnesota-Morris
Il Do Ha: Pukyong National University
Sangjin Kim: Dong-A University

Computational Statistics, 2025, vol. 40, issue 9, No 28, 5649-5676

Abstract: Abstract This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such data. Through simulation studies and analysis of real breast cancer data, our proposed CNN-LSTM with copula-based activation functions for multivariate multi-types of survival responses enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns. The model’s performance is evaluated using Shewhart control charts, focusing on the average run length (ARL).

Date: 2025
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DOI: 10.1007/s00180-025-01669-4

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