Global Sensitivity Analysis enabled Uncertainty Domain Reduction for Euglycemic Control of Type 1 Diabetics

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The scourge of diabetes afflicts over ten percent of the US population. In 2017, the cost of diabetes care was estimated to be over $300 billion. Five to ten percent of those diagnosed are Type 1 diabetic who constantly monitor their blood glucose to regulate it with insulin for survival. There have been profound improvements over the past few decades in continuous glucose monitors and insulin pumps, which now make it feasible to deploy an artificial pancreas. This device aims to emulate the function of a healthy pancreas and thus reduce the burden of managing the ailment for diabetics. The tremendous variability across diabetics in how the body responds to insulin and the fear of extremely low blood glucose (which can be fatal) mandates the development of robust controllers to account for this variability. This grant supports fundamental research to model well-recognized daily variation in insulin response characteristics and synthesize automated meal detection algorithms. This, in conjunction with comprehensive global sensitivity analysis, will enable the development of deployable real-time controllers for blood-glucose regulation. Results from this research will help reduce the cost of health care for millions of diabetics and improve the patient’s productivity, helping the US economy and well-being of society.To realize the full potential of an artificial pancreas, the research supported by this grant will model diurnal insulin sensitivity, which includes the dawn and dusk phenomena, and develop a meal detection algorithm to respond to unannounced meals, or rectify incorrectly counted carbohydrates in announced meals. These two goals will enhance the existing glucose-insulin models. However, the increased complexity of the proposed model will be burdened with increase in the dimension of the uncertain parameters. This mandates the development of a framework for non-moment based global sensitivity metrics to rank order parameters contributing the most to uncertainty in forecasting blood-glucose concentration. The resulting reduced dimension of the uncertain space will permit accurate uncertainty quantification and aid the development of robust controllers that maximize the time-in-range performance metric for blood-glucose regulation. A Food and Drug Administration approved software for validating the developed controllers and a 300-patient dataset will buttress the research outcomes. Although the immediate beneficiaries of the research are Type 1 diabetics, once matured, the research has the potential to also benefit people with Type 2 diabetes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.