AuthorsB. Leon, V. Naumova, E. Ruiz-Velazquez, A. McCulloch and E. Sanchez
TitleCombination of Neural Inverse Optimal Control with a Kernel-Based Regularization Learning Algorithm to Prevent Hypoglycemia in Type 1 Diabetes Patients
AfilliationScientific Computing
Project(s)Center for Biomedical Computing (SFF)
StatusSubmitted
Publication TypeJournal Article
Year of Publication2016
JournalIEEE Transactions on Neural Networks and Learning Systems
PublisherIEEE
Abstract

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).

Citation Key24492

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