{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:18:34Z","timestamp":1783455514139,"version":"3.55.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Major Program of the National Social Science Fund of China","award":["21&ZD128"],"award-info":[{"award-number":["21&ZD128"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1109\/jbhi.2025.3639425","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T18:49:11Z","timestamp":1764701351000},"page":"5642-5655","source":"Crossref","is-referenced-by-count":0,"title":["A Self-Adaptive Mixup-Augmented Selective Prediction Framework: A Case Study on In-Hospital Mortality Prediction"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2372-4629","authenticated-orcid":false,"given":"Kaidi","family":"Gong","sequence":"first","affiliation":[{"name":"Future Research Laboratory, China Mobile Research Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5133-9712","authenticated-orcid":false,"given":"Xiaolei","family":"Xie","sequence":"additional","affiliation":[{"name":"Future Research Laboratory, China Mobile Research Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.1957.5222035"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2017.2974"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000004924"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2020-100220"},{"key":"ref5","first-page":"4885","article-title":"Selective classification for deep neural networks","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Geifman","year":"2017"},{"key":"ref6","first-page":"2494","article-title":"AUC-based selective classification","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Pugnana","year":"2023"},{"key":"ref7","first-page":"10623","article-title":"Deep gamblers: Learning to abstain with portfolio theory","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Ziyin","year":"2019"},{"key":"ref8","first-page":"19365","article-title":"Self-adaptive training: Beyond empirical risk minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Huang","year":"2020"},{"key":"ref9","first-page":"21520","article-title":"Towards better selective classification","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Feng","year":"2023"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1970.1054406"},{"key":"ref11","first-page":"2151","article-title":"SelectiveNet: A deep neural network with an integrated reject option","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Geifman","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3727633"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3238024"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102435"},{"key":"ref15","first-page":"1823","article-title":"Classification with a reject option using a hinge loss","volume":"9","author":"Bartlett","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46379-7_5"},{"key":"ref17","first-page":"1507","article-title":"Classification with rejection based on cost-sensitive classification","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Charoenphakdee","year":"2021"},{"key":"ref18","first-page":"7076","article-title":"Consistent estimators for learning to defer to an expert","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Mozannar","year":"2020"},{"key":"ref19","first-page":"22184","article-title":"Calibrated learning to defer with one-vs-all classifiers","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","volume":"162","author":"Verma","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2124"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26133"},{"issue":"11","key":"ref22","first-page":"1","article-title":"Optimal strategies for reject option classifiers","volume":"24","author":"Franc","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref23","first-page":"6405","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Lakshminarayanan","year":"2017"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104418"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1542"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/hcs2.31"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3476088"},{"issue":"17","key":"ref28","first-page":"1","article-title":"Deep neural network benchmarks for selective classification","volume":"1","author":"Pugnana","year":"2024","journal-title":"J. Data-Centric Mach. Learn. Res."},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87735-4_11"},{"issue":"3","key":"ref30","article-title":"Selective prediction with long short-term memory using unit-wise batch standardization for time series health data sets: Algorithm development and validation","volume-title":"JMIR Med. Inform.","volume":"10","author":"Nam","year":"2022"},{"key":"ref31","first-page":"6234","article-title":"Combating label noise in deep learning using abstention","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Thulasidasan","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1029\/2021MS002573"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86340-1_23"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000215112.84523.F0"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1097\/00003246-199811000-00016"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000201881.58644.41"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.103"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0103-9"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106466"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-25472-z"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102437"},{"key":"ref42","doi-asserted-by":"crossref","DOI":"10.22271\/ed.book.2064","article-title":"An extensive data processing pipeline for MIMIC-IV","volume-title":"Proc. Mach. Learn. Res.","volume":"193","author":"Gupta","year":"2022"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-11012-2"},{"key":"ref44","first-page":"2866","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Zhang","year":"2018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw50498.2020.00010"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11595905\/11271776.pdf?arnumber=11271776","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:45:15Z","timestamp":1783453515000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11271776\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":46,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2025.3639425","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]}}}