{"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/learning-to-rank-for-censored-survival-data","title":"Learning to rank for censored survival data","arxiv_id":"1806.01984","date":"2018-06-06","proceeding":null,"authors":["Margaux Luck","Tristan Sylvain","Joseph Paul Cohen","Heloise Cardinal","Andrea Lodi","Yoshua Bengio"],"abstract":"Survival analysis is a type of semi-supervised ranking task where the target\noutput (the survival time) is often right-censored. Utilizing this information\nis a challenge because it is not obvious how to correctly incorporate these\ncensored examples into a model. We study how three categories of loss\nfunctions, namely partial likelihood methods, rank methods, and our\nclassification method based on a Wasserstein metric (WM) and the non-parametric\nKaplan Meier estimate of the probability density to impute the labels of\ncensored examples, can take advantage of this information. The proposed method\nallows us to have a model that predict the probability distribution of an\nevent. If a clinician had access to the detailed probability of an event over\ntime this would help in treatment planning. For example, determining if the\nrisk of kidney graft rejection is constant or peaked after some time. Also, we\ndemonstrate that this approach directly optimizes the expected C-index which is\nthe most common evaluation metric for ranking survival models.","url_abs":"http://arxiv.org/abs/1806.01984v2","url_pdf":"http://arxiv.org/pdf/1806.01984v2.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":"learning-to-rank-for-censored-survival-data","repo_url":"https://github.com/Museau/Survival_Pytorch_EMD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.01984","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}