{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:30:03Z","timestamp":1778257803522,"version":"3.51.4"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Elizabeth Weiser Caswell Diabetes Institute at the University of Michigan"},{"name":"JDRF Center of Excellence at U of M grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Biomed. Eng."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tbme.2024.3424665","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T17:51:12Z","timestamp":1720720272000},"page":"3424-3431","source":"Crossref","is-referenced-by-count":5,"title":["Shortcomings in the Evaluation of Blood Glucose Forecasting"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5341-8529","authenticated-orcid":false,"given":"Jung Min","family":"Lee","sequence":"first","affiliation":[{"name":"Division of Computer Science and Engineering, University of Michigan, USA"}]},{"given":"Rodica","family":"Pop-Busui","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8147-5168","authenticated-orcid":false,"given":"Joyce M.","family":"Lee","sequence":"additional","affiliation":[{"name":"Susan B. Meister Child Health Evaluation and Research Center, Division of Pediatric Endocrinology, University of Michigan, USA"}]},{"given":"Jesper","family":"Fleischer","sequence":"additional","affiliation":[{"name":"Steno Diabetes Center Aarhus, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1057-7722","authenticated-orcid":false,"given":"Jenna","family":"Wiens","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, University of Michigan, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)60591-7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-573-6_1"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCS.2016.2584318"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2337\/dc13-0251"},{"issue":"2","key":"ref5","first-page":"255","article-title":"Challenges and recent progress in the development of a closed-loop artificial pancreas","volume-title":"Annu. Rev. Control","volume":"36","author":"Bequette","year":"2012"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2337\/db11-0654"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102923"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2009.0076"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037460"},{"key":"ref10","first-page":"109","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume-title":"Artif. Intell. Med.","volume":"98","author":"Woldaregay","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3144870"},{"key":"ref12","first-page":"71","article-title":"The OhioT1DM dataset for blood glucose level prediction: Update","volume-title":"CEUR Workshop Proc.","volume":"2675","author":"Marling","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2908488"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2931842"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1021\/ie2004779"},{"key":"ref16","first-page":"2555","article-title":"Learning latent dynamics for planning from pixels","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Hafner","year":"2019"},{"key":"ref17","first-page":"3145","article-title":"Can autonomous vehicles identify, recover from, and adapt to distribution shifts?","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Filos","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/aic.11699"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3101589"},{"key":"ref20","first-page":"172","article-title":"Learning insulin-glucose dynamics in the wild","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Miller","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-44155-x"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2023.3331297"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2020.397"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1177\/193229680900300106"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1177\/1932296813514502"},{"key":"ref26","article-title":"simglucose","author":"Xie","year":"2022"},{"key":"ref27","first-page":"105","article-title":"Deep residual time-series forecasting: Application to blood glucose prediction","volume-title":"Proc. 5th Int. Workshop Knowl. Discovery Healthcare Data","author":"Rubin-Falcone","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1177\/19322968221092785"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.15439\/2019F159"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/s21217090"},{"key":"ref31","article-title":"LoopDocs","year":"2017"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8463189"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.2478\/acs-2018-0025"},{"key":"ref34","first-page":"213","article-title":"Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control","volume-title":"Procedia Comput. Sci.","volume":"216","author":"Wijaya","year":"2023"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1177\/193229680700100603"},{"key":"ref36","first-page":"508","article-title":"Deep reinforcement learning for closed-loop blood glucose control","volume-title":"Proc. 5th Mach. Learn. Healthcare Conf.","author":"Fox","year":"2020"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.2337\/dc17-1624"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2337\/dci19-0028"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.4.12.370"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2185234"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.10.5.622"}],"container-title":["IEEE Transactions on Biomedical Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10\/10762826\/10596103.pdf?arnumber=10596103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T03:13:39Z","timestamp":1735874019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10596103\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":41,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tbme.2024.3424665","relation":{},"ISSN":["0018-9294","1558-2531"],"issn-type":[{"value":"0018-9294","type":"print"},{"value":"1558-2531","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}