Development of attention-based LSTM models for the prediction of nocturnal hypoglycemia in patients with Type 1 Diabetes

Tziavaras, Konstantinos; Τζιβάρας Κωνσταντίνος

dc.contributor.author	Tziavaras, Konstantinos	en
dc.contributor.author	Τζιβάρας Κωνσταντίνος	el
dc.date.accessioned	2025-04-09T09:11:22Z
dc.date.available	2025-04-09T09:11:22Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61659
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29355
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Μεταφραστική Βιοιατρική Μηχανικής και Επιστήμης"	el
dc.rights	Default License
dc.subject	Μηχανική Μάθηση	el
dc.subject	Νυχτερινή Υπογλυκαιμία	el
dc.subject	Βαθιά Μάθηση	el
dc.subject	Μοντέλο Πρόβλεψης Κινδύνου	el
dc.subject	Σακχαρώδης Διαβήτης Τύπου 1	el
dc.subject	Machine Learning	en
dc.subject	Nocturnal Hypoglycemia	en
dc.subject	Deep Learning	en
dc.subject	Risk Prediction Model	en
dc.subject	Type 1 Diabetes	en
dc.title	Development of attention-based LSTM models for the prediction of nocturnal hypoglycemia in patients with Type 1 Diabetes	en
heal.type	masterThesis
heal.classification	Biomedical Engineering	en
heal.classification	Machine Learning	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-14
heal.abstract	Diabetes Mellitus (DM) is a chronic condition with a rising global prevalence and severe complications. The International Diabetes Federation projects that the number of individuals with diabetes will reach 643 million by 2030. To enhance glycemic control and mitigate the risk of serious physical and emotional complications related to hypoglycemia, this thesis presents the design, development, and evaluation of an interpretable model for predicting the risk of nocturnal hypoglycemic episodes in individuals with Type 1 Diabetes (T1DM). The proposed model employs a hybrid approach, integrating compartmental models with machine learning techniques. The OhioT1DM dataset, which includes real data from the eight-week monitoring period of 12 patients, was utilized for both development and evaluation purposes. Input data for the model consisted of glucose measurements, insulin doses, and meal information from the previous 24 hours. Mathematical models for simulating (i) the physiological mechanisms of insulin absorption from the subcutaneous tissue into the bloodstream, (ii) the activation of the insulin signaling pathway, and (iii) the absorption of glucose from the intestine were combined with Long Short-Term Memory Neural Networks (LSTMs). A custom attention layer was integrated to enhance the model’s performance and provide insights into the model’s reasoning behind its predictions. The model was assessed in terms of its ability to correctly predict nocturnal hypoglycemic events within a twelve-hour prediction window. Moreover, the Monte Carlo Dropout method was applied to quantify the uncertainty of the model's predictions. The model was also evaluated on an external dataset from the ten-day monitoring period of 12 T1DM patients, which was granted from the Diabetes Center, First Department of Pediatrics, P. & A. Kyriakou Children’s Hospital, Athens, within the framework of the SMARTDIAB project.	en
heal.advisorName	Nikita, Konstantina	en
heal.committeeMemberName	Nikita, Konstantina	en
heal.committeeMemberName	Stamou, Giorgos	en
heal.committeeMemberName	Voulodimos, Athanasios	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Συστημάτων Μετάδοσης Πληροφορίας και Τεχνολογίας Υλικών. Εργαστήριο Βιοϊατρικών Προσομοιώσεων και Απεικονιστικής Τεχνολογίας	el
heal.academicPublisherID	ntua
heal.numberOfPages	97 σ.	el
heal.fullTextAvailability	false