{"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/adversarial-time-to-event-modeling","title":"Adversarial Time-to-Event Modeling","arxiv_id":"1804.03184","date":"2018-04-09","proceeding":"ICML 2018 7","authors":["Paidamoyo Chapfuwa","Chenyang Tao","Chunyuan Li","Courtney Page","Benjamin Goldstein","Lawrence Carin","Ricardo Henao"],"abstract":"Modern health data science applications leverage abundant molecular and\nelectronic health data, providing opportunities for machine learning to build\nstatistical models to support clinical practice. Time-to-event analysis, also\ncalled survival analysis, stands as one of the most representative examples of\nsuch statistical models. We present a deep-network-based approach that\nleverages adversarial learning to address a key challenge in modern\ntime-to-event modeling: nonparametric estimation of event-time distributions.\nWe also introduce a principled cost function to exploit information from\ncensored events (events that occur subsequent to the observation window).\nUnlike most time-to-event models, we focus on the estimation of time-to-event\ndistributions, rather than time ordering. We validate our model on both\nbenchmark and real datasets, demonstrating that the proposed formulation yields\nsignificant performance gains relative to a parametric alternative, which we\nalso propose.","url_abs":"http://arxiv.org/abs/1804.03184v2","url_pdf":"http://arxiv.org/pdf/1804.03184v2.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":"adversarial-time-to-event-modeling","repo_url":"https://github.com/paidamoyo/adversarial_time_to_event","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"adversarial-time-to-event-modeling","repo_url":"https://github.com/avinashbarnwal/Synthetic-Data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"adversarial-time-to-event-modeling","repo_url":"https://github.com/paidamoyo/counterfactual_survival_analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"adversarial-time-to-event-modeling","repo_url":"https://github.com/paidamoyo/survival_cluster_analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03184","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}