{"id":5497,"date":"2025-07-22T07:00:26","date_gmt":"2025-07-22T07:00:26","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/22\/2507-14641\/"},"modified":"2025-07-22T07:00:26","modified_gmt":"2025-07-22T07:00:26","slug":"2507-14641","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/22\/2507-14641\/","title":{"rendered":"Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction"},"content":{"rendered":"<p>    Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2507.14641v1 Announce Type: new<br \/>\nAbstract: 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&#8217;s performance is evaluated using Shewhart control charts, focusing on the average run length (ARL).<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Jong-Min Kim, Il Do Ha, Sangjin Kim<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2507.14641\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction arXiv:2507.14641v1 Announce Type: new 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 [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[189,2817,233],"class_list":["post-5497","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-based","tag-copula","tag-survival"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5497"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5497"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5497\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5497"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5497"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}