Gaussian Process-Based Model Predictive Control of Blood Glucose for Patients with Type 1 Diabetes Mellitus

TL;DR

This paper introduces a Gaussian Process-based Model Predictive Control system that adapts to circadian insulin sensitivity changes for better blood glucose regulation in Type 1 Diabetes patients, demonstrated through simulation studies.

Contribution

It presents a novel control scheme that predicts and adapts to time-varying insulin sensitivity using Gaussian Processes and online learning.

Findings

Enhanced blood glucose control in simulations

Effective adaptation to circadian insulin sensitivity changes

Improved performance during fasting and meal scenarios

Abstract

The insulin sensitivity (IS) of the human body changes with a circadian rhythm. This adds to the time-varying feature of the glucose metabolism process and places challenges on the blood glucose (BG) control of patients with Type 1 Diabetes Mellitus. This paper presents a Model Predictive Controller that takes the periodic IS into account, in order to enhance BG control. The future effect of the IS is predicted using a machine learning technique, namely, a customized Gaussian Process (GP), based on historical training data. The training data for the GP is continuously updated during closed-loop control, which enables the control scheme to learn and adapt to intra-individual and inter-individual changes of the circadian IS rhythm. The necessary state information is provided by an Unscented Kalman Filter. The closed-loop performance of the proposed control scheme is evaluated for different scenarios (including fasting, announced meals and skipped meals) through in silico studies on simulation models of G\"ottingen Minipigs.