{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:48:28Z","timestamp":1780930108635,"version":"3.54.1"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["RS\u20102023\u201000240794"],"award-info":[{"award-number":["RS\u20102023\u201000240794"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Stat Anal Data Min: An ASA Data Sci Journal"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study introduces a novel approach to modeling competing risks in survival analysis by integrating learnable Copula functions (Clayton, Frank, and Gaussian) with deep learning architectures, including Convolutional Neural Networks (CNN), Long Short\u2010Term Memory (LSTM) networks, and a hybrid CNN\u2010LSTM model. Here, we are interested in classifying competing risks outcomes. The proposed method captures complex dependencies within the data. Our approach demonstrates improved predictive performance in survival data modeling by effectively capturing intricate dependency structures and event relationships. We validate the proposed models using both simulated data and real\u2010world clinical data. This research highlights the potential of integrating Copula\u2010based dependency structures into deep learning models for survival analysis with competing risks. The results emphasize how Copula\u2010based neural networks can enhance prediction accuracy and handle competing risks in survival analysis.<\/jats:p>","DOI":"10.1002\/sam.70051","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T10:47:48Z","timestamp":1763549268000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Copula\u2010Based Deep Learning Models for Competing Risks"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3821-2060","authenticated-orcid":false,"given":"Jong\u2010Min","family":"Kim","sequence":"first","affiliation":[{"name":"Statistics Discipline, Division of Science and Mathematics University of Minnesota\u2010Morris  Morris Minnesota USA"},{"name":"EGADE Business School, Tecnol\u00f3gico de Monterrey  Monterrey 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