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.

该提案的目的是为下一代多变量自动化开发原型 通过整合系统工程和 人工智能（AL）技术将减轻餐，体育锻炼的影响 心理压力诱导和睡眠不规则，没有用户手动输入的情况 调节糖尿病患者的葡萄糖水平。第一代自动胰岛素输送 （AID）依赖混合闭环技术的系统，从连续葡萄糖监测中收集数据 设备和需要手动用户输入来减轻餐和运动的影响。多变量 我们开发的援助提供了一个整合的下一代系统，可以分析历史和实时 来自不同来源的数据，包括连续的葡萄糖监测系统，胰岛素泵和 腕带体育锻炼跟踪器中的可穿戴传感器，以减轻饭菜，体育锻炼的影响 和急性心理压力，没有用户手动输入。餐，计划的练习，许多 日常生活，急性心理压力和睡眠不规则的体育活动会影响血糖 水平有差异，挑战1型糖尿病的人不断考虑所有这些复杂 维持其血糖水平在目标范围内的因素。葡萄糖的进一步改善 可以通过开发可以进行新颖，可解释和互动的技术来实现调节 向医疗服务提供者和援助用户解释他们的预测，并通过整合这些AL技术 采用系统工程技术来开发AL-M-VAID系统。这些技术的功能 是根据历史趋势和当前数据来预测一个人的状态，并提供其他 MVAID系统的有价值信息，以减轻用户的解释重复性任务 他们当前的代谢状态，预测其当前行动对葡萄糖未来变化的影响 级别，并调整Al-Mvaid控制器的参数。目标是生产强大的用户友好 将新颖的AL技术与MVAID系统集成到最小用户负担的技术 尽管有许多复杂的血糖干扰，但在自由度中发生了许多复杂的血糖障碍，但仍能达到严格控制葡萄糖水平 饮食，体育锻炼，急性心理压力和睡眠不规则等疾病。