{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-for-patient-specific-kidney","title":"Deep Learning for Patient-Specific Kidney Graft Survival Analysis","arxiv_id":"1705.10245","date":"2017-05-29","proceeding":null,"authors":["Margaux Luck","Tristan Sylvain","Héloïse Cardinal","Andrea Lodi","Yoshua Bengio"],"abstract":"An accurate model of patient-specific kidney graft survival distributions can\nhelp to improve shared-decision making in the treatment and care of patients.\nIn this paper, we propose a deep learning method that directly models the\nsurvival function instead of estimating the hazard function to predict survival\ntimes for graft patients based on the principle of multi-task learning. By\nlearning to jointly predict the time of the event, and its rank in the cox\npartial log likelihood framework, our deep learning approach outperforms, in\nterms of survival time prediction quality and concordance index, other common\nmethods for survival analysis, including the Cox Proportional Hazards model and\na network trained on the cox partial log-likelihood.","url_abs":"http://arxiv.org/abs/1705.10245v1","url_pdf":"http://arxiv.org/pdf/1705.10245v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-for-patient-specific-kidney","repo_url":"https://github.com/EbnulMahmood/PatternLab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-learning-for-patient-specific-kidney","repo_url":"https://github.com/yishuwei2019/DL-based-dynamic-risk-prediciton","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.10245","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}