Garcia-Tirado Jose, Corbett John P, Boiroux Dimitri, Jørgensen John Bagterp, Breton Marc D
Center for Diabetes Technology, University of Virginia, Charlottesville, VA.
Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA.
J Process Control. 2019 Aug;80:202-210. doi: 10.1016/j.jprocont.2019.05.017. Epub 2019 Jun 23.
This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 /), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC < 0.05).
本文提出了一种个性化的集成模型预测控制（EnMPC）算法，用于在经常运动的1型糖尿病（T1D）患者中稳定血糖（BG）并预防低血糖。EnMPC公式可被视为一种简化的多阶段MPC，允许考虑从患者近期行为中收集的情景。患者的身体活动行为通过从患者连续血糖监测仪（CGM）的反卷积中得出的特定运动输入信号来表征，该信号考虑了已知输入，如膳食和胰岛素泵记录。EnMPC控制器在来自FDA认可的UVA/帕多瓦模拟平台的具有代表性的受试者间和受试者内变异性的一组患者中进行了测试。结果表明，与类似调整的基线控制器（rMPC）相比，在轻度至中度运动30分钟后，低血糖预防有显著改善；低血糖发生率（<70 /）从rMPC的3.08%±3.55降至EnMPC的0.78%±2.04（<0.05）。
