{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T13:24:32Z","timestamp":1785936272429,"version":"3.56.0"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P00993X\/1"],"award-info":[{"award-number":["EP\/P00993X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000761","name":"Imperial College London","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000761","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Biomed. Eng."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/tbme.2022.3187703","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T19:30:21Z","timestamp":1656703821000},"page":"193-204","source":"Crossref","is-referenced-by-count":122,"title":["Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9782-3470","authenticated-orcid":false,"given":"Taiyu","family":"Zhu","sequence":"first","affiliation":[{"name":"Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3073-3128","authenticated-orcid":false,"given":"Kezhi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London, London, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7088-5807","authenticated-orcid":false,"given":"Pau","family":"Herrero","sequence":"additional","affiliation":[{"name":"Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2476-3857","authenticated-orcid":false,"given":"Pantelis","family":"Georgiou","sequence":"additional","affiliation":[{"name":"Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2019.107843"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-8587(16)30010-9"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2337\/dc16-2584"},{"issue":"14","key":"ref4","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1056\/NEJMoa0805017","article-title":"Juvenile diabetes research foundation continuous glucose monitoring study group, continuous glucose monitoring and intensive treatment of type 1 diabetes","volume":"359","year":"2008","journal-title":"New England J. Med."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2015.0417"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2058"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2588"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/electronics3040609"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.889774"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.2833"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.2975959"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.07.007"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3040225"},{"key":"ref14","first-page":"74","article-title":"A deep learning algorithm for personalized blood glucose prediction","volume-title":"Proc. 3rd Int. Workshop Knowl. Discov. Healthcare Data","author":"Zhu","year":"2018"},{"key":"ref15","first-page":"69","article-title":"Dilated recurrent neural network for short-time prediction of glucose concentration","volume-title":"Proc. 3rd Int. Workshop Knowl. Discov. Healthcare Data","author":"Chen","year":"2018"},{"key":"ref16","first-page":"105","article-title":"Deep residual time-series forecasting: Application to blood glucose prediction","volume-title":"Proc. 5th Int. Workshop Knowl. Discov. Healthcare Data","author":"Rubin-Falcone","year":"2020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103255"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2636665"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"ref21","first-page":"14927","article-title":"Deep evidential regression","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"33","author":"Amini","year":"2020"},{"key":"ref22","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref24","first-page":"1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume-title":"Proc. NIPS Workshop Deep Learn.","author":"Chung","year":"2014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/NEUREL.2018.8586990"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2946693"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.neuro.26.041002.131047"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045336"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3019893"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref32","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vaswani","year":"2017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8856940"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2931842"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2908488"},{"issue":"3","key":"ref37","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1007\/s41666-020-00068-2","volume":"4","author":"Zhu","year":"2020","journal-title":"J. Healthcare Informat. Res."},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3037693"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-019-00059-y"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref41","first-page":"90","article-title":"Blood glucose prediction for type 1 diabetes using generative adversarial networks","volume-title":"Proc. 5th Int. Workshop Knowl. Discov. Healthcare Data","author":"Zhu","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1177\/1932296820922622"},{"key":"ref43","first-page":"3183","article-title":"Evidential deep learning to quantify classification uncertainty","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Sensoy","year":"2018"},{"key":"ref44","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref45","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref46","first-page":"71","article-title":"The OhioT1DM dataset for blood glucose level prediction: Update 2020","volume-title":"Proc. 5th KDH Workshop","author":"Marling","year":"2020"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2020.0387"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2012.6346567"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2219876"},{"key":"ref50","first-page":"1","article-title":"A machine learning approach to predicting blood glucose levels for diabetes management","volume-title":"Proc. Workshops at 28th AAAI Conf. Artif. Intell.","author":"Plis","year":"2014"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380128"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.10.5.622"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2185234"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"S104","DOI":"10.1016\/j.jcjd.2017.10.010","article-title":"Hypoglycemia","volume":"42","author":"Yale","year":"2018","journal-title":"Can. J. Diabetes"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00480-x"},{"key":"ref56","first-page":"1","article-title":"Rapid learning or feature reuse? towards understanding the effectiveness of MAML","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Raghu","year":"2019"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1177\/19322968211042621"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2020.0061"},{"key":"ref59","first-page":"15427","article-title":"Certified monotonic neural networks","volume-title":"Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.3390\/s19194338"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2823763"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1177\/1460458219850682"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.3049109"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00626-5"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.103888"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-53352-6_5"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.3390\/s20185058"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3014556"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1177\/1932296815588945"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3143375"}],"container-title":["IEEE Transactions on Biomedical Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10\/9998557\/09813400.pdf?arnumber=9813400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:07:29Z","timestamp":1706760449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9813400\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":70,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tbme.2022.3187703","relation":{},"ISSN":["0018-9294","1558-2531"],"issn-type":[{"value":"0018-9294","type":"print"},{"value":"1558-2531","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}