{"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-recurrent-survival-analysis","title":"Deep Recurrent Survival Analysis","arxiv_id":"1809.02403","date":"2018-09-07","proceeding":null,"authors":["Kan Ren","Jiarui Qin","Lei Zheng","Zhengyu Yang","Wei-Nan Zhang","Lin Qiu","Yong Yu"],"abstract":"Survival analysis is a hotspot in statistical research for modeling\ntime-to-event information with data censorship handling, which has been widely\nused in many applications such as clinical research, information system and\nother fields with survivorship bias. Many works have been proposed for survival\nanalysis ranging from traditional statistic methods to machine learning models.\nHowever, the existing methodologies either utilize counting-based statistics on\nthe segmented data, or have a pre-assumption on the event probability\ndistribution w.r.t. time. Moreover, few works consider sequential patterns\nwithin the feature space. In this paper, we propose a Deep Recurrent Survival\nAnalysis model which combines deep learning for conditional probability\nprediction at fine-grained level of the data, and survival analysis for\ntackling the censorship. By capturing the time dependency through modeling the\nconditional probability of the event for each sample, our method predicts the\nlikelihood of the true event occurrence and estimates the survival rate over\ntime, i.e., the probability of the non-occurrence of the event, for the\ncensored data. Meanwhile, without assuming any specific form of the event\nprobability distribution, our model shows great advantages over the previous\nworks on fitting various sophisticated data distributions. In the experiments\non the three real-world tasks from different fields, our model significantly\noutperforms the state-of-the-art solutions under various metrics.","url_abs":"http://arxiv.org/abs/1809.02403v2","url_pdf":"http://arxiv.org/pdf/1809.02403v2.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-recurrent-survival-analysis","repo_url":"https://github.com/rk2900/drsa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02403","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}