Shi Dawei, Dassau Eyal, Doyle Francis J
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge MA 02138.
Bioeng Transl Med. 2018 Nov 12;4(1):61-74. doi: 10.1002/btm2.10119. eCollection 2019 Jan.
The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.
长期使用人工胰腺（AP）需要一种能够随着患者的生长、发育和生活方式变化进行学习和适应的自动胰岛素输送算法。在这项工作中，我们引入了一种数据驱动的AP适应方法，以改善家庭环境中的血糖管理。提出了一种两阶段贝叶斯优化辅助参数学习算法，用于调整基础胰岛素和碳水化合物比例曲线以及关键反馈控制参数。该方法基于美国食品药品监督管理局（FDA）认可的UVA/帕多瓦1型糖尿病模拟器的111名成人队列，通过三种存在生活方式干扰和初始参数不正确的场景进行评估。对于所有场景，所提出的方法能够稳健地调整AP参数，以提高血糖调节性能，即处于正常血糖范围[70, 180]mg/dl的时间百分比，同时不会导致低血糖风险，即血糖低于70mg/dl的时间百分比。
