Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction
Machine Learning
2025-07-22 v1 Machine Learning
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).
Cite
@article{arxiv.2507.14641,
title = {Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction},
author = {Jong-Min Kim and Il Do Ha and Sangjin Kim},
journal= {arXiv preprint arXiv:2507.14641},
year = {2025}
}