Hybrid Modeling of the Insulin-Glucose System: Combining Linear and Data-Driven Models for Artificial Pancreas

Abstract

Type 1 diabetes mellitus is a chronic condition that requires insulin delivery to maintain blood glucose levels within a desired range. The artificial pancreas (AP), which integrates a continuous glucose sensor, an insulin pump, and a control algorithm, is a promising solution for automating insulin delivery. Designing optimal controllers for the AP is crucial to its effectiveness. Existing approaches often rely on advanced controllers based on models of the insulin-glucose system. However, this system is highly complex, nonlinear, and subject to time-varying dynamics and inter-patient variability, which pose significant challenges for model accuracy and control design. Hence, data-driven and machine learning-based models are emerging as powerful alternatives. This paper presents a novel data-driven modeling approach that combines two components: a linear model and a machine learning-based model. This latter is computed with the CHoKI learning method, to capture the nonlinear deviations of the actual system from the linear model, enabling the combined model to better represent the insulin-glucose system. This hybrid modeling approach offers improved prediction accuracy compared to previously proposed models in the literature. The improved model accuracy can lead to better controllers for the AP. The proposed approach is validated using the virtual patients of the FDA-accepted UVA/Padova simulator. The results outperform state-of-the-art models in prediction errors, demonstrating its potential as a step forward in AP control system design.