A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas

Abstract

Background and objectives: Complete identification of the glucose dynamics for a patient generally re-quires prior clinical procedures and several measurements for the patient. However, these steps may not be always feasible. To address this limitation, we propose a practical approach integrating learning-based model predictive control (MPC), adaptive basal and bolus injections, and suspension with minimal re-quirements of prior knowledge of the patient. Methods: The glucose dynamic system matrices were periodically updated using only input values, with-out any pretrained models. The optimal insulin dose was calculated based on a learning-based MPC al-gorithm. Meal detection and estimation modules were also introduced. The basal and bolus insulin in-jections were fine-tuned using the performance of glucose control from the previous day. To validate the proposed method, evaluations with 20 virtual patients from a type 1 diabetes metabolic simulator were employed. Results: Time-in-range (TIR) and time-below-range (TBR) were 90.8% (84.1% - 95.6%) and 0.3% (0% - 0.8%), as represented by the median, first (Q1), and third quartiles (Q3), respectively, when meal intakes were fully announced. When one out of three meal intake announcements was missing, TIR and TBR were 85.2% (75.0% - 88.9%) and 0.9% (0.4% - 1.1%), respectively. Conclusions: The proposed approach obviates the need for prior tests from patients and shows effective regulation of blood glucose levels. From the perspective of practical implementation in clinical environ-ments, to deal with minimal prior information of the patient, our study demonstrates how essential clin-ical knowledge and learning-based modules can be integrated into a control framework for an artificial pancreas. & COPY; 2023 Elsevier B.V. All rights reserved.