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dc.contributor.authorŞahin, Asiye
dc.contributor.authorAydın, Ahmet
dc.date.accessioned2021-06-21T10:48:28Z
dc.date.available2021-06-21T10:48:28Z
dc.date.issued2021en_US
dc.identifier.citationŞahin, A., Aydın, A. (2021). Personalized Advanced Time Blood Glucose Level Prediction. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-05263-2en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-020-05263-2
dc.identifier.urihttps://hdl.handle.net/20.500.12508/1776
dc.description.abstractThe introduction of continuous glucose monitoring (CGM) devices for glucose level measurement accelerated the application of artificial intelligence methods in predicting advanced time blood glucose levels by providing lots of continuous structured data needed to train the methods. Advanced time blood glucose level prediction enables diabetic patients to better manage their blood glucose levels and receive early warnings about the wrong treatments and adverse conditions such as hypoglycemia or hyperglycemia. In this study, an artificial neural network is trained for 30- and 60-min prediction horizon by using physiological models for insulin injection, carbohydrate intake, and physical activity in addition to past CGM data for each of six real T1D patients. The mean of the prediction error for six patients is obtained as 18.81 mg/dL and 30.89 mg/dL for 30- and 60-min prediction horizons, respectively. These results are better than the other studies in the literature that use real patient data, and the model is computationally simpler compared to the deep learning-based methods. Therefore, in this study, a model that can be implemented on the mobile or embedded device, learn the patient’s physiologic dynamics, and make accurate predictions during the measurements is developed and presented.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s13369-020-05263-2en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBlood glucose level predictionen_US
dc.subjectContinuous glucose monitoringen_US
dc.subjectPhysiological modelsen_US
dc.subjectArtificial neural networksen_US
dc.subjectCorrelation analysisen_US
dc.subject.classificationArtificial Pancreas
dc.subject.classificationHypoglycemia
dc.subject.classificationInsulin Dependent Diabetes Mellitus
dc.subject.classificationMultidisciplinary Sciences
dc.titlePersonalized Advanced Time Blood Glucose Level Predictionen_US
dc.typearticleen_US
dc.relation.journalArabian Journal for Science and Engineeringen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Biyomedikal Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorŞahin, Asiye
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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