{"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/a-scalable-discrete-time-survival-model-for","title":"A Scalable Discrete-Time Survival Model for Neural Networks","arxiv_id":"1805.00917","date":"2018-05-02","proceeding":null,"authors":["Michael F. Gensheimer","Balasubramanian Narasimhan"],"abstract":"There is currently great interest in applying neural networks to prediction\ntasks in medicine. It is important for predictive models to be able to use\nsurvival data, where each patient has a known follow-up time and\nevent/censoring indicator. This avoids information loss when training the model\nand enables generation of predicted survival curves. In this paper, we describe\na discrete-time survival model that is designed to be used with neural\nnetworks, which we refer to as Nnet-survival. The model is trained with the\nmaximum likelihood method using minibatch stochastic gradient descent (SGD).\nThe use of SGD enables rapid convergence and application to large datasets that\ndo not fit in memory. The model is flexible, so that the baseline hazard rate\nand the effect of the input data on hazard probability can vary with follow-up\ntime. It has been implemented in the Keras deep learning framework, and source\ncode for the model and several examples is available online. We demonstrate the\nperformance of the model on both simulated and real data and compare it to\nexisting models Cox-nnet and Deepsurv.","url_abs":"http://arxiv.org/abs/1805.00917v3","url_pdf":"http://arxiv.org/pdf/1805.00917v3.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":"a-scalable-discrete-time-survival-model-for","repo_url":"https://github.com/MGensheimer/nnet-survival","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-scalable-discrete-time-survival-model-for","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":"survival-analysis","task_name":"Survival Analysis"}],"methods":[{"method_slug":"sgd","method_name":"SGD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.00917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.00917"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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