A Data-Driven Approach to Glycemic Disturbance Mitigation, Reconstruction, and Pattern Recognition

Corbett, John, Systems Engineering - School of Engineering and Applied Science, University of Virginia
Breton, Marc, MD-PSCH Psychiatry and NB Sciences, University of Virginia

The outline of the following chapters is listed below.

Chapter 1 states the problem that is addressed by this dissertation and the contents of each of the specific aims.

Chapter 2 provides background on the physiology of glucose homeostasis in the human body. A review of diabetes pathology, simulation modeling of type one diabetes, challenges with self-reported data, and meal detection is also included.

Chapter 3 describes the design and evaluation of an automatic bolus priming system focused on safety. This chapter also includes the results of a pilot clinical study where the automatic bolus priming system was integrated into an MPC-based automatic insulin dosing system and compared to a state-of-the-art artificial pancreas control system.

Chapter 4 explains the design of two glycemic disturbance detection algorithms. There is also a comparison of these two algorithms provided in this chapter.

Chapter 5 details how historical data was used to create individualized profiles representing patterns of disturbances experienced by people with type 1 diabetes and how these profiles were integrated into a multistage model predictive control system to anticipate glycemic disturbances such as meals. The results of a simulation experiment designed to evaluate the impact of the bolus priming system and the anticipatory disturbance profiles on glycemia are also discussed.

Chapter 6 summarizes the findings of this dissertation and reflects on its impact. Additionally, there are some further applications of this work that are presented.

PHD (Doctor of Philosophy)
pattern recognition, meal detection, artificial pancreas, type 1 diabetes, diabetes, closed-loop, automated insulin delivery
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