Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction

• URL: http://arxiv.org/abs/2507.14641v1
• Date: Sat, 19 Jul 2025 14:35:51 GMT
• Title: Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction
• Authors: Jong-Min Kim, Il Do Ha, Sangjin Kim,
• Abstract summary: This research integrates deep learning, copula functions, and survival analysis.<n>It introduces copula-based activation functions to model the nonlinear dependencies inherent in such data.<n>Our proposed CNN-LSTM enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns.
• Score: 0.7087571536842716
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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