Data-driven Modeling for Diabetes

This contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students.

Hypoglycemia Prevention using Low Glucose Suspend Systems.- Linear Modeling and Prediction in Diabetes Physiology.- Adaptive Algorithms for Personalized Diabetes Treatment.- Data-driven modeling of Diabetes Progression.- Nonlinear Modeling of the Dynamic Effects of Free Fatty Acids on Insulin Sensitivity.- Data-driven and Mininal-type Compartmental Insulin-Glucose Models: Theory and Applications.- Pitfalls in model identification: examples from Glucose-Insulin modelling.- Ensemble Glucose Prediction in Insulin-Dependent Diabetes.- Simple parameters describing gut absorption and lipid dynamics in relation to glucose metabolism during a routine oral glucose test.- Simulation Models for In-Silico Evaluation of Closed-Loop Insulin Delivery Systems in Type 1 Diabetes