{"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/deepsurv-personalized-treatment-recommender","title":"DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network","arxiv_id":"1606.00931","date":"2016-06-02","proceeding":null,"authors":["Jared Katzman","Uri Shaham","Jonathan Bates","Alexander Cloninger","Tingting Jiang","Yuval Kluger"],"abstract":"Medical practitioners use survival models to explore and understand the\nrelationships between patients' covariates (e.g. clinical and genetic features)\nand the effectiveness of various treatment options. Standard survival models\nlike the linear Cox proportional hazards model require extensive feature\nengineering or prior medical knowledge to model treatment interaction at an\nindividual level. While nonlinear survival methods, such as neural networks and\nsurvival forests, can inherently model these high-level interaction terms, they\nhave yet to be shown as effective treatment recommender systems. We introduce\nDeepSurv, a Cox proportional hazards deep neural network and state-of-the-art\nsurvival method for modeling interactions between a patient's covariates and\ntreatment effectiveness in order to provide personalized treatment\nrecommendations. We perform a number of experiments training DeepSurv on\nsimulated and real survival data. We demonstrate that DeepSurv performs as well\nas or better than other state-of-the-art survival models and validate that\nDeepSurv successfully models increasingly complex relationships between a\npatient's covariates and their risk of failure. We then show how DeepSurv\nmodels the relationship between a patient's features and effectiveness of\ndifferent treatment options to show how DeepSurv can be used to provide\nindividual treatment recommendations. Finally, we train DeepSurv on real\nclinical studies to demonstrate how it's personalized treatment recommendations\nwould increase the survival time of a set of patients. The predictive and\nmodeling capabilities of DeepSurv will enable medical researchers to use deep\nneural networks as a tool in their exploration, understanding, and prediction\nof the effects of a patient's characteristics on their risk of failure.","url_abs":"http://arxiv.org/abs/1606.00931v3","url_pdf":"http://arxiv.org/pdf/1606.00931v3.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":"deepsurv-personalized-treatment-recommender","repo_url":"https://github.com/jaredleekatzman/DeepSurv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deepsurv-personalized-treatment-recommender","repo_url":"https://github.com/sschrod/bites","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"deepsurv-personalized-treatment-recommender","repo_url":"https://github.com/czifan/DeepSurv.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deepsurv-personalized-treatment-recommender","repo_url":"https://github.com/havakv/pycox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"predicting-patient-outcomes","task_name":"Predicting Patient Outcomes"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.00931","atlas_url":"https://app.syntology.ai/?focus=1606.00931","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.00931"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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