{"ID":2892842,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14641","arxiv_id":"2507.14641","title":"Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction","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).","short_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. Thro...","url_abs":"https://arxiv.org/abs/2507.14641","url_pdf":"https://arxiv.org/pdf/2507.14641v1","authors":"[\"Jong-Min Kim\",\"Il Do Ha\",\"Sangjin Kim\"]","published":"2025-07-19T14:35:51Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}