{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:05:50Z","timestamp":1764842750398,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031345852"},{"type":"electronic","value":"9783031345869"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34586-9_30","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:27:04Z","timestamp":1686367624000},"page":"450-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Exploratory Study of the Value of Vital Signs on the Short-Term Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes \u2013 The GlucoseML Study"],"prefix":"10.1007","author":[{"given":"Daphne N.","family":"Katsarou","sequence":"first","affiliation":[]},{"given":"Eleni I.","family":"Georga","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Christou","sequence":"additional","affiliation":[]},{"given":"Stelios","family":"Tigas","sequence":"additional","affiliation":[]},{"given":"Costas","family":"Papaloukas","sequence":"additional","affiliation":[]},{"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"30_CR1","unstructured":"Frayn, K.N.: Metabolic Regulation: A Human Perspective. 3rd edn., pp. 306\u2013308. Wiley-Blackwell, UK (2010)"},{"issue":"11","key":"30_CR2","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.2337\/dci21-0043","volume":"44","author":"RIG Holt","year":"2021","unstructured":"Holt, R.I.G., et al.: The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European association for the study of diabetes (EASD). Diab. Care 44(11), 2589\u20132625 (2021)","journal-title":"Diab. Care"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee: 6. Glycemic targets: standards of medical care in diabetes. Diab. Care 45(Supplement_1), S83\u2013S96 (2022)","DOI":"10.2337\/dc22-S006"},{"issue":"5","key":"30_CR4","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s00125-020-05366-3","volume":"64","author":"SA Amiel","year":"2021","unstructured":"Amiel, S.A.: The consequences of hypoglycaemia. Diabetologia 64(5), 963\u2013970 (2021). https:\/\/doi.org\/10.1007\/s00125-020-05366-3","journal-title":"Diabetologia"},{"issue":"2","key":"30_CR5","doi-asserted-by":"publisher","first-page":"316","DOI":"10.2337\/dc14-0920","volume":"38","author":"K Khunti","year":"2014","unstructured":"Khunti, K., et al.: Hypoglycemia and risk of cardiovascular disease and all-cause mortality in insulin-treated people with type 1 and type 2 diabetes: a cohort study. Diab. Care 38(2), 316\u2013322 (2014)","journal-title":"Diab. Care"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Tsichlaki, S., et al.: Type 1 diabetes hypoglycemia prediction algorithms: systematic review. JMIR Diab. 7(3), e34699 (2022)","DOI":"10.2196\/34699"},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.artmed.2021.102120","volume":"118","author":"V Felizardo","year":"2021","unstructured":"Felizardo, V., et al.: Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction \u2013 a systematic literature review. Artif. Intell. Med. 118, 102\u2013120 (2021)","journal-title":"Artif. Intell. Med."},{"issue":"7","key":"30_CR8","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/JBHI.2019.2956704","volume":"24","author":"E Montaser","year":"2020","unstructured":"Montaser, E., et al.: Seasonal local models for glucose prediction in type 1 diabetes. IEEE J. Biomed. Health Inform. 24(7), 2064\u20132072 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"30_CR9","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12911-021-01462-5","volume":"21","author":"MF Rabby","year":"2021","unstructured":"Rabby, M.F., et al.: Stacked LSTM based deep recurrent neural network with Kalman smoothing for blood glucose prediction. BMC Med. Inf. Decis. Making 21(1), 101 (2021)","journal-title":"BMC Med. Inf. Decis. Making"},{"issue":"2","key":"30_CR10","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/TBME.2019.2919250","volume":"67","author":"M Schiavon","year":"2020","unstructured":"Schiavon, M., et al.: Modeling subcutaneous absorption of long-acting insulin glargine in type 1 diabetes. IEEE Trans. Biomed. Eng. 67(2), 624\u2013631 (2020)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"14","key":"30_CR11","doi-asserted-by":"publisher","first-page":"4926","DOI":"10.3390\/s21144926","volume":"21","author":"M Mu\u00f1oz-Organero","year":"2021","unstructured":"Mu\u00f1oz-Organero, M., et al.: Learning carbohydrate digestion and insulin absorption curves using blood glucose level prediction and deep learning models. Sens. (Basel) 21(14), 4926 (2021)","journal-title":"Sens. (Basel)"},{"issue":"11","key":"30_CR12","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1109\/TBME.2020.2975959","volume":"67","author":"J Xie","year":"2020","unstructured":"Xie, J.: Benchmarking machine learning algorithms on blood glucose prediction for type I diabetes in comparison with classical time-series models. IEEE Trans. Biomed. Eng. 67(11), 3101\u20133124 (2020)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Rodr\u00edguez, I., et al.: On the possibility of predicting glycaemia \u2018on the fly\u2019 with constrained IoT devices in type 1 diabetes mellitus patients. Sens. (Basel) 19(20), 4538 (2019)","DOI":"10.3390\/s19204538"},{"issue":"14","key":"30_CR14","doi-asserted-by":"publisher","first-page":"3870","DOI":"10.3390\/s20143870","volume":"20","author":"M Vettoretti","year":"2020","unstructured":"Vettoretti, M., et al.: Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors. Sensors 20(14), 3870 (2020)","journal-title":"Sensors"},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1089\/dia.2019.0435","volume":"22","author":"A Yotam","year":"2020","unstructured":"Yotam, A., et al.: Clinically accurate prediction of glucose levels in patients with type 1 diabetes. Diab. Technol. Therap. 22, 562\u2013569 (2020)","journal-title":"Diab. Technol. Therap."},{"key":"30_CR16","unstructured":"Brownlee, J.: Deep Learning for Time series Forecasting. Machine Learning Mastery (2018)"},{"issue":"2","key":"30_CR17","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10994-005-5828-3","volume":"58","author":"Y Shou","year":"2005","unstructured":"Shou, Y., et al.: Fst and exact warping of time series using adaptive segmental approximations. Mach. Learn. 58(2), 231\u2013267 (2005)","journal-title":"Mach. Learn."},{"issue":"8","key":"30_CR18","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.2337\/diacare.27.8.1922","volume":"27","author":"BP Kovatchev","year":"2004","unstructured":"Kovatchev, B.P., et al.: Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose\u2013error grid analysis illustrated by TheraSense freestyle navigator data. Diab. Care 27(8), 1922\u20131928 (2004)","journal-title":"Diab. Care"},{"issue":"2","key":"30_CR19","doi-asserted-by":"publisher","first-page":"132","DOI":"10.2337\/dc11-s220","volume":"34","author":"BM Frier","year":"2011","unstructured":"Frier, B.M., et al.: Hypoglycemia and cardiovascular risks. Diab. Care 34(2), 132\u2013137 (2011)","journal-title":"Diab. Care"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Pervasive Computing Technologies for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34586-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:34:24Z","timestamp":1686368064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34586-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031345852","9783031345869"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34586-9_30","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pervasive Computing Technologies for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ph2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pervasivehealth.eai-conferences.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy Plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}