Prediction Methods for Blood Glucose Concentration

Harald Kirchsteiger is postdoctoral researcher at the Johannes Kepler University in Linz, Austria. He finished his PhD on model-based control of blood glucose concentration in 2012 and continued working intensively on data-based modeling of the glucose/insulin metabolism. John Bagterp Jørgensen is an associate professor in Scientific Computing at Department of Applied Mathematics and Computer Science at the Technical University of Denmark. He is also a faculty member at the Technical University of Denmark's Center for Energy Resources Engineering (CERE). His research focus is concentrated on Model Predictive Control including computational aspects and applications. The applications include industrial processes, intelligent control of smart energy systems, production optimization and closed-loop reservoir management of oil fields, and an artificial pancreas for people with type 1 diabetes. His research is to a large extent conducted in collaboration with industrial companies. Eric Renard is professor of endocrinology, diabetes and metabolic diseases at the University of Montpellier I, France. He is also head of the Department of Endocrinology, Diabetes, Nutrition and the Clinical Investigation Center INSERM 1411 at Montpellier University Hospital. Since more than 20 years, his research group has been at the forefront of the transfer of technologies toward diabetes management, including wearable and implantable insulin pumps, continuous glucose monitoring and artificial pancreas models. Luigi del Re is professor at the Johannes Kepler University in Linz, Austria, in the field of modeling and control of dynamical systems with special interest in complex nonlinear systems both from the control and the optimization point of view. He has been working in biomedical applications since 1992.

From the Contents : Part I Introduction.- Clinical Relevance of Glucose Prediction: Needs and Goals.- What is the Technical Challenge of Blood Glucose Prediction?.- Part II Possible Solutions.- An Overview of Glucose Prediction Algorithms.- Data-Based Interval Models Employing Continuous-Time System Identification.