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

该提案的目的是开发下一代多变量自动化的原型， 胰岛素输送（mvAID）系统（也称为人工胰腺）， 人工智能（AI）技术，将减轻膳食，体力活动，急性 心理压力诱因和睡眠不规律，而无需用户手动输入， 调节糖尿病患者的血糖水平。第一代自动胰岛素输注 (AID)系统依赖于混合闭环技术，从连续葡萄糖监测中收集数据 设备，并且需要手动用户输入以减轻进餐和锻炼的影响。多变量 我们开发的AID提供了一个集成良好的下一代系统， 来自不同来源的数据，包括持续葡萄糖监测系统、胰岛素泵和 腕带式身体活动跟踪器中的可穿戴传感器，以减轻进餐、身体活动 和急性心理压力而无需用户的手动输入。膳食，有计划的锻炼，许多 日常生活的体力活动、急性心理应激和睡眠不规律会影响血糖 水平差异，挑战1型糖尿病患者不断考虑所有这些复杂的 将血糖水平维持在目标范围内的因素。葡萄糖进一步改善 可以通过开发新颖的、可解释的和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AI技术， 用系统工程技术来开发一个Al-mvAID系统。这些人工智能技术的作用 是根据历史趋势和当前数据预测一个人的状态，并提供额外的 为mvAID系统提供有价值的信息，使用户从繁重的重复口译任务中解脱出来 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。我们的目标是产生一个强大的用户友好的 一种将新AI技术与mvAID系统集成在一起的技术， 尽管在自由生活中发生了许多复杂的血糖紊乱， 条件，如膳食，身体活动，急性心理压力和睡眠不规律。

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.