SCH: Integrating AI and System Engineering for Glucose Regulation in Diabetes

SCH：整合人工智能和系统工程来调节糖尿病的血糖

• 批准号: 10706604
• 负责人: Ali Cinar
• 金额: $ 29.87万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706604
• 关键词: Accelerometer; Activities of Daily Living; Acute; Affect; Algorithms; Artificial Endocrine Pancreas; Artificial Intelligence; Artificial Pancreas; Behavior; Blood Glucose; Clinical; Clinical Research; Compensation; Complex; Continuous Glucose Monitor; Data; Data Analyses; Data Reporting; Decision Making; Diabetes Mellitus; Dose; Engineering; Event; Exercise; Foundations; Fright; Future; Generations; Glucose; Goals; Habits; Hybrids; Hypoglycemia; Individual; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Learning; Length; Life; Machine Learning; Manuals; Medical; Metabolic; Modeling; Modification; National Institute of Diabetes and Digestive and Kidney Diseases; Patients; Pattern; Performance; Personal Behavior; Persons; Physical activity; Physiological; Play; Psychological Stress; Pump; Regulation; Reporting; Research; Resources; Role; Sleep; Sleep disturbances; Source; Stream; Stress; System; Techniques; Technology; Time; Update; Variant; Work; blood glucose regulation; care providers; complex data; euglycemia; experimental study; glycemic control; hands-on learning; human-in-the-loop; improved; monitoring device; next generation; novel; prototype; response; sedentary; sensor; simulation; success; trend; user-friendly; wearable device; wearable sensor technology

The objective of this proposal is to develop a prototype for the next generation multivariable automated insulin delivery (mvAID) systems (also called artificial pancreas) by integrating systems engineering and artificial intelligence (Al) techniques that will mitigate the effects of meals, physical activities ,acute psychological stress inducements and sleep irregularities without manual inputs by the user to tightly regulate the glucose levels of people with diabetes. The first generation of automated insulin delivery (AID) systems relied on hybrid closed-loop technology, collecting data from continuous glucose monitoring devices and requiring manual user inputs for mitigating the effects of meals and exercise. The multivariable AID that we developed provides a well-integrated next-generation system that analyzes historical and realtime data from different sources, including continuous glucose monitoring systems, insulin pumps, and wearable sensors in wristband physical activity trackers, to mitigate the effects of meals, physical activities, and acute psychological stress without manual inputs by the user. Meals, planned exercises, many physical activities of daily living, acute psychological stress, and sleep irregularities affect blood glucose levels differentially, challenging people with Type 1 diabetes to continuously consider all these complex factors in maintaining their blood glucose levels in the target range. Further improvement in glucose regulation can be achieved by developing novel, interpretable, and interactive Al techniques that can explain their predictions to medical care providers and AID users, and by integrating these Al techniques with systems engineering techniques to develop an Al-mvAID system. The function of these Al techniques is to predict the state of a person based on historical trends and current data, and provide additional valuable information to the mvAID system to relieve the users from onerous repetitive tasks for interpreting their current metabolic state, predicting the impact of their current actions on future variations in glucose levels, and tuning the parameters of the Al-mvAID controller. The goal is to produce a powerful userfriendly technology that integrates novel Al techniques with mvAID systems for minimal user burden in achieving tight control of glucose levels despite the many complex glycemic disturbances occurring in freeliving conditions, such as meals, physical activities, acute psychological stress, and sleep irregularities.

该提案的目标是开发下一代多变量自动化原型 胰岛素输送（mvAID）系统（也称为人工胰腺）通过集成系统工程和 人工智能 (Al) 技术将减轻膳食、体力活动、急性发作的影响 心理压力诱因和睡眠不规律，无需用户手动输入来严格控制 调节糖尿病患者的血糖水平。第一代自动胰岛素输送 (AID) 系统依靠混合闭环技术，从连续血糖监测中收集数据 设备并需要用户手动输入来减轻膳食和运动的影响。多变量 我们开发的 AID 提供了一个集成良好的下一代系统，可以分析历史和实时 来自不同来源的数据，包括连续血糖监测系统、胰岛素泵和 腕带身体活动追踪器中的可穿戴传感器，以减轻膳食、身体活动、 以及严重的心理压力，无需用户手动输入。膳食、计划好的锻炼等等 日常生活的体力活动、严重的心理压力和睡眠不规律都会影响血糖 水平差异，挑战 1 型糖尿病患者不断考虑所有这些复杂的因素 将血糖水平维持在目标范围内的因素。血糖进一步改善 可以通过开发新颖的、可解释的、交互式的人工智能技术来实现监管 向医疗保健提供者和 AID 用户解释他们的预测，并通过整合这些人工智能技术 利用系统工程技术开发 Al-mvAID 系统。这些AI技术的作用 就是根据历史趋势和当前数据来预测一个人的状态，并提供额外的信息 向 mvAID 系统提供有价值的信息，将用户从繁重的重复性口译任务中解放出来 他们当前的代谢状态，预测他们当前的行为对未来葡萄糖变化的影响 水平，并调整 Al-mvAID 控制器的参数。目标是产生一个功能强大、用户友好的 将新颖的人工智能技术与 mvAID 系统相结合的技术，可最大限度地减少用户负担 尽管自由生活中发生许多复杂的血糖紊乱，但仍能严格控制血糖水平 状况，例如膳食、体力活动、严重的心理压力和睡眠不规律。