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- W4313338936 abstract "Type 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease involving high blood glucose levels. It is caused by the destruction of pancreatic <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$beta$</tex> -cells, which are responsible for insulin production. Survival of subjects with T1DM is dependent on exogenous insulin administrations [1]. In order to calculate and then inject an appropriate insulin dose, subjects must frequently monitor their glucose values, taking into account not only the exact amount of carbohydrates ingested, but also physical activity [2], [3], disease states [4], and stress episodes [5]. The main goal of glycemic control is to avoid or at least mitigate events such as hypoglycemia and hyperglycemia [6]. Hypoglycemia, defined as a blood glucose level lower than <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$70 mg/dL$</tex> , can result in serious immediate complications such as seizure or coma [7], whereas hyperglycemia, defined as a blood glucose level greater than <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$180 mg/dL$</tex> , leads to several microvascular and macrovascular complications in the long term [7]. The advent of Continuous Glucose Monitoring (CGM) devices, i.e., sensors capable of measuring interstitial glucose concentration at 1-to 5-minute intervals, has improved the quality of life of individuals with T1DM, allowing a decrease in the occurrence of hypo/hyperglycemic events [8]. Despite the widespread use of these devices, adverse glycemic events still occur. Providing patients with a prediction of blood glucose levels can further improve their quality of life enabling proactive interventions and reducing their daily cognitive load [9]. Indeed, research is focusing on the development of data-driven models that can predict future glycemic levels as accurately as possible on both short-term [10] and long-term predictions [11], leveraging past CGM values alone [12] or in combination with other features such as insulin injections or carbohydrate intake [13]." @default.
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- W4313338936 date "2022-11-16" @default.
- W4313338936 modified "2023-09-27" @default.
- W4313338936 title "Identification of Optimal Training for Prediction of Glucose Levels in Type-1-Diabetes Using Edge Computing" @default.
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- W4313338936 doi "https://doi.org/10.1109/iceccme55909.2022.9988026" @default.
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