|Authors||B. Leon, V. Naumova, E. Ruiz-Velazquez, A. McCulloch and E. Sanchez|
|Title||Combination of Neural Inverse Optimal Control with a Kernel-Based Regularization Learning Algorithm to Prevent Hypoglycemia in Type 1 Diabetes Patients|
|Project(s)||Center for Biomedical Computing (SFF)|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
Hypoglycemia periods in Type 1 Diabetes mellitus (T1DM) patients are a dangerous condition leading to serious acute complications, such as diabetic coma or death.
Despite recent technological and scientific advances in T1DM therapy management, prevention of severe hypoglycemic periods still remains a challenge.
In this paper, we present a novel combination of a neural inverse optimal control via control Lyapunov function (CLF) combined with a kernel-based regularization learning
predictive algorithm (KAR) for optimal control of the blood glucose levels with a strong focus on timely detection and prevention of acute debilitating and harmful hypoglycemic
events. We describe how the proposed scheme can be used for aforementioned problem and report the results of the tests
on University of Virginia (UVA)/Padova Simulator as well as comparing them with existing literature. The performance assessment of the algorithms has been made with the use of control variability grid analysis (CVGA).