Insulin Metabolism Models for Children with Type 1 Diabetes

Content Provider	Semantic Scholar
Author	Mougiakakou, Stavroula G.
Copyright Year	2016
Abstract	IntroductIon Diabetes Mellitus (DM) is a chronic metabolic disease resulted from insufficient secretion of hormone insulin. DM is mainly classified into Type 1 (or insulin dependent diabetes), which is characterized by absence of insulin secretion, due to destruction of the β-cells of pancreas, and Type 2 (or insulin independent diabetes), which is characterized by reduced action of insulin. This dysfunction of insulin results in many short-(hypoglycaemia, hyperglycaemia) and long-term (like neuropathies, nephropathies, retinopathies, and so on) complications. indicate that the intensive glucaemic control reduces many short-and long-term complications of DM. The ultimate goal in management of DM is the development of an automatic " closed-loop " system, well known as " artificial pancreas, " able to simulate accurately the glucose-insulin metabolism and maintain glucose levels of a diabetes patient into physiological levels (70–110 mg/dl for a healthy person). A " closed-loop " system for DM comprises three primary components: (i) an accurate frequent or continuous glucose measurement system, (ii) an insulin delivery system, and (iii) a control system able to change the dose of delivered insulin, with respect to the requirement of glucose levels in desired range. Currently, individuals with Type 1 Diabetes Mellitus (T1DM) measure their glucose levels using either conventional finger-stick glucose meters (three to four times per day), or continuous glucose monitoring systems (CGMS), and choose their insulin delivery method between multiple daily injection (MDI), and continuous subcutaneous insulin infusion (CSII). The most appropriate algorithms to close the loop seems to be those based on model predictive control—MPC (Bequette, 2005). In a MPC controller, a model is used for the prediction of current and future insulin delivery parameters based on estimation on future glucose concentrations, while an optimizer finds the optimum insulin delivery parameter values in order to maintain future glucose values inside the desired range. The models for the predictions of glucose values simulate the glucose-insulin metabolic system, which is characterized by high complexity and nonlinearity. Several mathematical models (MMs) have been proposed for the simulation of glucose-insulin metabolism for T1DM patients taking into consideration previous glucose measurements, type, and dose of insulin intake , and food intake, while recently, artificial neural networks (NN) have been proposed for simulation of glucose—insulin metabolism. I The aim of this article is to describe how NN have been applied for the simulation of glucose—insulin metabolism, and to present two NN based personalized models for children with T1DM. The models, …
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Language	English
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Subject Keyword	Action (physics) Algorithm Artificial neural network CGMS-A CNS disorder Chomsky hierarchy Classification Control system Diabetes Mellitus Diabetes Mellitus, Insulin-Dependent Diabetes Mellitus, Non-Insulin-Dependent Glucose Metabolism Disorders Hyperglycemia Insulin Lispro Kidney Diseases Mathematical Model Mathematical optimization Mathematics Mesenchymal Stem Cells Metabolic Diseases Metabolic Process, Cellular Multiple Endocrine Neoplasia Multiple document interface Nonlinear system Pancreas extract Pancreas, Artificial Patients Personalization Population Parameter Retinal Diseases System of measurement insulin metabolic process insulin secretion insulin, isophane nervous system disorder
Content Type	Text
Resource Type	Article