{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:27:59Z","timestamp":1791484079129,"version":"4.3.4"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.cmpb.2024.108438","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T19:39:50Z","timestamp":1727293190000},"page":"108438","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"special_numbering":"C","title":["Forecasting glucose values for patients with type 1 diabetes using heart rate data"],"prefix":"10.1016","volume":"257","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3906-9214","authenticated-orcid":false,"given":"Raffaele","family":"Giancotti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1431-6044","authenticated-orcid":false,"given":"Pietro","family":"Bosoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0014-478X","authenticated-orcid":false,"given":"Patrizia","family":"Vizza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1367-8676","authenticated-orcid":false,"given":"Giuseppe","family":"Tradigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agostino","family":"Gnasso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5542-2997","authenticated-orcid":false,"given":"Pietro Hiram","family":"Guzzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Bellazzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Concetta","family":"Irace","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierangelo","family":"Veltri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cmpb.2024.108438_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nrdp.2017.16","article-title":"Type 1 diabetes mellitus","volume":"3","author":"Katsarou","year":"2017","journal-title":"Nat. Rev. Dis. Primers"},{"issue":"9513","key":"10.1016\/j.cmpb.2024.108438_b2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/S0140-6736(06)68341-4","article-title":"Type 1 diabetes","volume":"367","author":"Daneman","year":"2006","journal-title":"Lancet"},{"issue":"12","key":"10.1016\/j.cmpb.2024.108438_b3","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.2337\/dci17-0043","article-title":"Improving the clinical value and utility of cgm systems: issues and recommendations: a joint statement of the european association for the study of diabetes and the american diabetes association diabetes technology working group","volume":"40","author":"Petrie","year":"2017","journal-title":"Diabetes Care"},{"issue":"S2","key":"10.1016\/j.cmpb.2024.108438_b4","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1089\/dia.2015.0417","article-title":"Continuous glucose monitoring: a review of successes, challenges, and opportunities","volume":"18","author":"Rodbard","year":"2016","journal-title":"Diabetes Technol. Ther."},{"issue":"5","key":"10.1016\/j.cmpb.2024.108438_b5","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.2337\/diacare.28.5.1231","article-title":"Continuous glucose monitoring: roadmap for 21st century diabetes therapy","volume":"28","author":"Klonoff","year":"2005","journal-title":"Diabetes Care"},{"issue":"14","key":"10.1016\/j.cmpb.2024.108438_b6","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1056\/NEJMoa0805017","article-title":"Continuous glucose monitoring and intensive treatment of type 1 diabetes","volume":"359","year":"2008","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.cmpb.2024.108438_b7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1111\/dom.15504","article-title":"The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management","volume":"26","author":"Reddy","year":"2024","journal-title":"Diabetes Obes. Metab."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108438_b8","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1177\/19322968221116393","article-title":"Detection of hypoglycemia and hyperglycemia using noninvasive wearable sensors: Electrocardiograms and accelerometry","volume":"18","author":"Dave","year":"2024","journal-title":"J. Diabetes Sci. Technol."},{"issue":"5","key":"10.1016\/j.cmpb.2024.108438_b9","doi-asserted-by":"crossref","first-page":"624","DOI":"10.2337\/diacare.23.5.624","article-title":"Heart rate in relation to insulin sensitivity and insulin secretion in nondiabetic subjects","volume":"23","author":"Festa","year":"2000","journal-title":"Diabetes Care"},{"issue":"6","key":"10.1016\/j.cmpb.2024.108438_b10","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1517\/13543784.3.6.561","article-title":"Section review: Oncologic, endocrine and metabolic adrenoceptors and imidazoline binding sites in the endocrine pancreas as targets for anti-hyperglycaemic drugs","volume":"3","author":"Morgan","year":"1994","journal-title":"Expert Opin. Investig. Drugs"},{"issue":"4","key":"10.1016\/j.cmpb.2024.108438_b11","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/S1056-8727(03)00031-X","article-title":"Poor glycemic control is associated with increased diastolic blood pressure and heart rate in children with type 1 diabetes","volume":"18","author":"Torchinsky","year":"2004","journal-title":"J. Diabetes Complicat."},{"issue":"8","key":"10.1016\/j.cmpb.2024.108438_b12","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.2337\/dc06-1441","volume":"30","author":"Paterson","year":"2007","journal-title":"Diabetes Care"},{"key":"10.1016\/j.cmpb.2024.108438_b13","doi-asserted-by":"crossref","unstructured":"Rahul Dey, Fathi M. Salem, Gate-variants of gated recurrent unit (gru) neural networks, in: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS, 2017, pp. 1597\u20131600.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"10.1016\/j.cmpb.2024.108438_b14","doi-asserted-by":"crossref","unstructured":"Muskaan Pirani, Paurav Thakkar, Pranay Jivrani, Mohammed Husain Bohara, Dweepna Garg, A comparative analysis of arima, gru, lstm and bilstm on financial time series forecasting, in: 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE, 2022, pp. 1\u20136.","DOI":"10.1109\/ICDCECE53908.2022.9793213"},{"key":"10.1016\/j.cmpb.2024.108438_b15","doi-asserted-by":"crossref","first-page":"20212","DOI":"10.1109\/ACCESS.2022.3149523","article-title":"Prediction of vehicle driver\u2019s facial air temperature with svr, ann, and gru","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.cmpb.2024.108438_b16","doi-asserted-by":"crossref","first-page":"5517","DOI":"10.1109\/TII.2022.3193414","article-title":"Gcn- and gru-based intelligent model for temperature prediction of local heating surfaces","volume":"19","author":"Chen","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"3","key":"10.1016\/j.cmpb.2024.108438_b17","first-page":"430","article-title":"Deep learning approach using the gru-lstm hybrid model for air temperature prediction on daily basis","volume":"10","author":"Sari","year":"2022","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"10.1016\/j.cmpb.2024.108438_b18","series-title":"A comprehensive overview and comparative analysis on deep learning models: Cnn, rnn, lstm, gru","author":"Shiri","year":"2023"},{"key":"10.1016\/j.cmpb.2024.108438_b19","series-title":"Artificial Intelligence in Medicine","article-title":"Deep learning applied to blood glucose prediction from flash glucose monitoring and fitbit data","author":"Bosoni","year":"2020"},{"key":"10.1016\/j.cmpb.2024.108438_b20","series-title":"CEUR Workshop Proceedings","first-page":"71","article-title":"The ohiot1dm dataset for blood glucose level prediction: Update 2020","volume":"Vol. 2675","author":"Marling","year":"2020"},{"key":"10.1016\/j.cmpb.2024.108438_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121994","article-title":"Iomt innovations in diabetes management: Predictive models using wearable data","volume":"238","author":"Rodr\u00edguez-Rodr\u00edguez","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"90","key":"10.1016\/j.cmpb.2024.108438_b22","first-page":"120","article-title":"Predicting glycemia in type 1 diabetes patients: experiments with xgboost","volume":"60","author":"Midroni","year":"2018","journal-title":"Heart"},{"key":"10.1016\/j.cmpb.2024.108438_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.health.2023.100273","article-title":"A risk assessment and prediction framework for diabetes mellitus using machine learning algorithms","author":"Bhat","year":"2023","journal-title":"Healthc. Anal."},{"key":"10.1016\/j.cmpb.2024.108438_b24","series-title":"Proceedings of the Annual Symposium on Computer Application in Medical Care","first-page":"261","article-title":"Using the adap learning algorithm to forecast the onset of diabetes mellitus","author":"Smith","year":"1988"},{"issue":"21","key":"10.1016\/j.cmpb.2024.108438_b25","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.3390\/app9214604","article-title":"Current techniques for diabetes prediction: review and case study","volume":"9","author":"Larabi-Marie-Sainte","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.cmpb.2024.108438_b26","doi-asserted-by":"crossref","unstructured":"PV Sankar Ganesh, P. Sripriya, A comparative review of prediction methods for pima indians diabetes dataset, in: Computational Vision and Bio-Inspired Computing: ICCVBIC 2019, 2020, pp. 735\u2013750.","DOI":"10.1007\/978-3-030-37218-7_83"},{"issue":"22","key":"10.1016\/j.cmpb.2024.108438_b27","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.3390\/s22228682","article-title":"Assessment of seasonal stochastic local models for glucose prediction without meal size information under free-living conditions","volume":"22","author":"Prendin","year":"2022","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108438_b28","doi-asserted-by":"crossref","first-page":"16865","DOI":"10.1038\/s41598-023-44155-x","article-title":"The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using shap","volume":"13","author":"Prendin","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2024.108438_b29","unstructured":"Jonas Freiburghaus, A\u00efcha Rizzotti, Fabrizio Albertetti, A deep learning approach for blood glucose prediction of type 1 diabetes, in: Proceedings of the Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), 29-30 August 2020, Santiago de Compostela, Spain, Vol. 2675, 2020, pp. 29\u201330, August 2020."},{"key":"10.1016\/j.cmpb.2024.108438_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104865","article-title":"Penalty weighted glucose prediction models could lead to better clinically usage","volume":"138","author":"Cichosz","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2024.108438_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104472","article-title":"Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model","volume":"151","author":"Cichosz","year":"2021","journal-title":"Int. J. Med. Inform."},{"issue":"16","key":"10.1016\/j.cmpb.2024.108438_b32","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1016\/j.neucom.2008.01.030","article-title":"Long-term time series prediction with the narx network: An empirical evaluation","volume":"71","author":"Menezes","year":"2008","journal-title":"Neurocomputing"},{"issue":"8","key":"10.1016\/j.cmpb.2024.108438_b33","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.3390\/en11082163","article-title":"Gated recurrent unit network-based short-term photovoltaic forecasting","volume":"11","author":"Wang","year":"2018","journal-title":"Energies"},{"key":"10.1016\/j.cmpb.2024.108438_b34","volume":"vol. 2148","year":"2018"},{"key":"10.1016\/j.cmpb.2024.108438_b35","series-title":"Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence, KDH@ECAI 2020, Santiago de Compostela, Spain & Virtually, August 29-30, 2020","volume":"vol. 2675","year":"2020"},{"key":"10.1016\/j.cmpb.2024.108438_b36","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/RBME.2023.3331297","article-title":"Artificial intelligence and machine learning for improving glycemic control in diabetes: Best practices, pitfalls, and opportunities","volume":"17","author":"Jacobs","year":"2024","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108438_b37","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.cmpb.2024.108438_b38","series-title":"2020 International Workshop on Electronic Communication and Artificial Intelligence","first-page":"98","article-title":"Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example","author":"Yang","year":"2020"},{"key":"10.1016\/j.cmpb.2024.108438_b39","series-title":"Effective approaches to attention-based neural machine translation","author":"Luong","year":"2015"},{"key":"10.1016\/j.cmpb.2024.108438_b40","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108438_b41","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0227222","article-title":"Forecasting stock prices with long-short term memory neural network based on attention mechanism","volume":"15","author":"Qiu","year":"2020","journal-title":"PLoS One"},{"issue":"12","key":"10.1016\/j.cmpb.2024.108438_b42","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1007\/s10409-021-01148-1","article-title":"Physics-informed neural networks (pinns) for fluid mechanics: A review","volume":"37","author":"Cai","year":"2021","journal-title":"Acta Mech. Sin."},{"key":"10.1016\/j.cmpb.2024.108438_b43","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sleep.2023.11.038","article-title":"Correlation analysis of heart rate variations and glucose fluctuations during sleep","volume":"113","author":"Kajisa","year":"2024","journal-title":"Sleep Med."},{"key":"10.1016\/j.cmpb.2024.108438_b44","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"5","key":"10.1016\/j.cmpb.2024.108438_b45","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1089\/dia.2005.7.776","article-title":"The original clarke error grid analysis (ega)","volume":"7","author":"Clarke","year":"2005","journal-title":"Diabetes Technol. Ther."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724004310?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724004310?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T02:28:26Z","timestamp":1740796106000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260724004310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":45,"alternative-id":["S0169260724004310"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108438","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Forecasting glucose values for patients with type 1 diabetes using heart rate data","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108438","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108438"}}