{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T07:49:53Z","timestamp":1768636193510,"version":"3.49.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1734913"],"award-info":[{"award-number":["1734913"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007812","name":"Biostatistics Shared Resource of the Fred Hutch\/University of Washington Cancer Consortium","doi-asserted-by":"publisher","award":["P30 CA015704"],"award-info":[{"award-number":["P30 CA015704"]}],"id":[{"id":"10.13039\/100007812","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3142032","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:37:24Z","timestamp":1641933444000},"page":"8005-8020","source":"Crossref","is-referenced-by-count":9,"title":["Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1592-9766","authenticated-orcid":false,"given":"Phawis","family":"Thammasorn","sequence":"first","affiliation":[]},{"given":"Stephanie K.","family":"Schaub","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2427-4404","authenticated-orcid":false,"given":"Daniel S.","family":"Hippe","sequence":"additional","affiliation":[]},{"given":"Matthew B.","family":"Spraker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-9853","authenticated-orcid":false,"given":"Jan C.","family":"Peeken","sequence":"additional","affiliation":[]},{"given":"Landon S.","family":"Wootton","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6461-3306","authenticated-orcid":false,"given":"Paul E.","family":"Kinahan","sequence":"additional","affiliation":[]},{"given":"Stephanie E.","family":"Combs","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8051-5981","authenticated-orcid":false,"given":"Wanpracha A.","family":"Chaovalitwongse","sequence":"additional","affiliation":[]},{"given":"Matthew J.","family":"Nyflot","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9876.00165"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950648"},{"key":"ref4","volume-title":"Multiple Regression and the Analysis of Variance and Covariance","author":"Edwards","year":"1985"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1977.10480613"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.6257"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2012.01.006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"ref10","article-title":"Continuous and discrete-time survival prediction with neural networks","volume-title":"arXiv:1910.06724","author":"Kvamme","year":"2019"},{"issue":"129","key":"ref11","first-page":"1","article-title":"Time-to-event prediction with neural networks and Cox regression","volume":"20","author":"Kvamme","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10985-007-9048-y"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1006\/jbin.2002.1038"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.01.004"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2019.02.003"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4154"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3214306"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2016.7822579"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23525-7_15"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476296"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOAS169"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2019.08.059"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.08.023"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13122866"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1177\/2040622321992624"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11080787"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-80262-9"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1111\/cts.13187"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12995"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00401"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101789"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.02.013"},{"key":"ref35","first-page":"184","article-title":"Deep parametric time-to-event regression with time-varying covariates","volume-title":"Proc. Mach. Learn. Res. (PMLR)","author":"Nagpal"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.6339\/21-JDS1018"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105012"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3052441"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2980204"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-020-0418-1"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/9668973\/9676588-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09676588.pdf?arnumber=9676588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:54:13Z","timestamp":1705186453000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9676588\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3142032","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}