{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:32:16Z","timestamp":1753882336605,"version":"3.41.2"},"reference-count":46,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CSE125741"],"award-info":[{"award-number":["CSE125741"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013373","name":"alberta machine intelligence institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013373","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["203245"],"award-info":[{"award-number":["203245"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p> Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia. They must also be careful not to inject too much insulin because this could induce (potentially fatal) hypoglycemia. Patients therefore follow a \u201cregimen\u201d that determines how much insulin to inject at each time, based on various measurements. We can produce an effective regimen if we can accurately predict a patient\u2019s future blood glucose (BG) values from his\/her current features. This study explores the challenges of predicting future BG by applying a number of machine learning algorithms, as well as various data preprocessing variations (corresponding to 312 [learner, preprocessed-dataset] combinations), to a new T1D dataset that contains 29,601 entries from 47 different patients. Our most accurate predictor, a weighted ensemble of two Gaussian Process Regression models, achieved a (cross-validation) [Formula: see text] loss of 2.7\u2009mmol\/L (48.65\u2009mg\/dl). This result was unexpectedly poor given that one can obtain an [Formula: see text] of 2.9\u2009mmol\/L (52.43\u2009mg\/dl) using the naive approach of simply predicting the patient\u2019s average BG. These results suggest that the diabetes diary data that is typically collected may be insufficient to produce accurate BG prediction models; additional data may be necessary to build accurate BG prediction models over hours. <\/jats:p>","DOI":"10.1177\/1460458220977584","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T08:17:05Z","timestamp":1611821825000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["The challenge of predicting blood glucose concentration changes in patients with type I diabetes"],"prefix":"10.1177","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5960-1236","authenticated-orcid":false,"given":"Neil C","family":"Borle","sequence":"first","affiliation":[{"name":"University of Alberta, Canada"}]},{"given":"Edmond A","family":"Ryan","sequence":"additional","affiliation":[{"name":"University of Alberta, Canada"},{"name":"Alberta Diabetes Institute, Canada"}]},{"given":"Russell","family":"Greiner","sequence":"additional","affiliation":[{"name":"University of Alberta, Canada"},{"name":"Alberta Machine Intelligence Institute, Canada"}]}],"member":"179","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"bibr1-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(06)68341-4"},{"volume-title":"Predicting blood glucose levels of diabetics using artificial neural networks","year":"2004","author":"Kok P.","key":"bibr2-1460458220977584"},{"volume-title":"Model-free intelligent diabetes management using machine learning","year":"2014","author":"Bastani M.","key":"bibr3-1460458220977584"},{"key":"bibr4-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2012.09.003"},{"volume-title":"The challenge of predicting future blood glucose for patients with type I diabetes","year":"2017","author":"Borle N.","key":"bibr5-1460458220977584"},{"key":"bibr6-1460458220977584","doi-asserted-by":"publisher","DOI":"10.2196\/10775"},{"key":"bibr7-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1109\/72.788659"},{"first-page":"3216","volume-title":"Engineering in medicine and biology society, 2007. EMBS 2007. 29th annual international conference of the IEEE","author":"Baghdadi G","key":"bibr8-1460458220977584"},{"key":"bibr9-1460458220977584","first-page":"72","volume":"5","author":"Zainuddin Z","year":"2009","journal-title":"Int J Comput Intell"},{"first-page":"182","volume-title":"Proceedings of the 2019 11th international conference on machine learning and computing","author":"Asad M","key":"bibr10-1460458220977584"},{"key":"bibr11-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2018.2844179"},{"first-page":"1124","volume-title":"2017 36th Chinese control conference (CCC)","author":"Sun X","key":"bibr12-1460458220977584"},{"first-page":"3262","volume-title":"Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE","author":"Georga EI","key":"bibr13-1460458220977584"},{"key":"bibr14-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2607(94)90052-3"},{"key":"bibr15-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1177\/193229680800200507"},{"first-page":"4913","volume-title":"Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE","author":"Valletta JJ","key":"bibr16-1460458220977584"},{"volume-title":"Intelligent diabetes assistant: a telemedicine system for modeling and managing blood glucose","year":"2010","author":"Duke DL.","key":"bibr17-1460458220977584"},{"key":"bibr18-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-015-1320-9"},{"key":"bibr19-1460458220977584","volume":"190107467","author":"Liu C","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"bibr20-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.06.005"},{"key":"bibr21-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.873388"},{"key":"bibr22-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2823763"},{"key":"bibr23-1460458220977584","doi-asserted-by":"publisher","DOI":"10.2337\/dc11-0366"},{"first-page":"1","volume-title":"Bioengineering conference (NEBEC), 2011 IEEE 37th annual Northeast","author":"Chemlal S","key":"bibr24-1460458220977584"},{"key":"bibr25-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1177\/1932296814554260"},{"key":"bibr26-1460458220977584","first-page":"135","volume-title":"Machine learning and applications (ICMLA), 2013 12th international conference on","volume":"1","author":"Bunescu R"},{"key":"bibr27-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1177\/193229681000400104"},{"key":"bibr28-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2010.0104"},{"volume-title":"Modern artificial intelligence for health analytics papers from the AAAI-14, 2014","author":"Plis K","key":"bibr29-1460458220977584"},{"first-page":"291","volume-title":"2018 16th IEEE international new circuits and systems conference (NEWCAS)","author":"Doike T","key":"bibr30-1460458220977584"},{"first-page":"1387","volume-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","author":"Fox I","key":"bibr31-1460458220977584"},{"key":"bibr32-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2185234"},{"key":"bibr33-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2005.7.776"},{"key":"bibr34-1460458220977584","unstructured":"Borle NC, Ryan EA, Greiner R. The challenge of predicting meal-to-meal blood glucose concentrations for patients with type i diabetes. arXiv preprint arXiv:190312347, 2019."},{"key":"bibr35-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcjd.2016.06.001"},{"first-page":"1051","volume-title":"Computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT\/IUCC\/DASC\/PICOM)","author":"Al-Taee AM","key":"bibr36-1460458220977584"},{"key":"bibr37-1460458220977584","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.22.9.1501"},{"key":"bibr38-1460458220977584","doi-asserted-by":"publisher","DOI":"10.2337\/diab.37.12.1608"},{"issue":"1","key":"bibr39-1460458220977584","first-page":"45","volume":"2","author":"Weisang G","year":"2008","journal-title":"Case Stud Bus Ind Govt Stat"},{"key":"bibr40-1460458220977584","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-92-1-59"},{"key":"bibr41-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1172\/JCI60016"},{"key":"bibr42-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1111\/dme.13347"},{"key":"bibr43-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1111\/dme.13863"},{"key":"bibr44-1460458220977584","doi-asserted-by":"publisher","DOI":"10.1111\/pedi.12640"},{"key":"bibr45-1460458220977584","doi-asserted-by":"publisher","DOI":"10.3109\/00365529209096020"},{"key":"bibr46-1460458220977584","doi-asserted-by":"publisher","DOI":"10.2337\/cd17-0130"}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1460458220977584","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1460458220977584","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1460458220977584","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T05:02:25Z","timestamp":1740805345000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1460458220977584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1177\/1460458220977584"],"URL":"https:\/\/doi.org\/10.1177\/1460458220977584","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"type":"print","value":"1460-4582"},{"type":"electronic","value":"1741-2811"}],"subject":[],"published":{"date-parts":[[2021,1]]},"article-number":"1460458220977584"}}