Online Archive of University of Virginia Scholarship
A Data-Driven Approach to Glycemic Disturbance Mitigation, Reconstruction, and Pattern Recognition284 views
Author
Corbett, John, Systems Engineering - School of Engineering and Applied Science, University of Virginia
Advisors
Breton, Marc, MD-PSCH Psychiatry and NB Sciences, University of Virginia
Abstract
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.
Corbett, John. A Data-Driven Approach to Glycemic Disturbance Mitigation, Reconstruction, and Pattern Recognition. University of Virginia, Systems Engineering - School of Engineering and Applied Science, PHD (Doctor of Philosophy), 2021-09-03, https://doi.org/10.18130/6ez8-8q05.
