Leveraging Big Data and Deep Learning to Develop Next Generation Decision Support Tools to Improve Glycemic Outcomes in Type 1 Diabetes

利用大数据和深度学习开发下一代决策支持工具以改善 1 型糖尿病的血糖结果

• 批准号: 10611369
• 负责人: Gavin Young
• 金额: $ 5.27万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611369
• 关键词: Acute; Address; Adult; Aerobic; Aerobic Exercise; Affect; Algorithms; Area; Artificial Intelligence; Behavior; Big Data; Blood Glucose; Carbohydrates; Clinic; Clinical Medicine; Clinical Trials; Compensation; Complex; Consensus; Continuous Glucose Monitor; Control Groups; Cutaneous; Dangerousness; Data; Data Set; Decision Support Systems; Diabetes Mellitus; Disease; Dose; Eating; Event; Exercise; Fright; Functional disorder; Future; Glucose; Goals; Guidelines; Hour; Human; Hybrids; Hypoglycemia; Injections; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Intake; Jogging; Mathematics; Mediating; Modeling; Modification; Outcome; Participant; Patients; Performance; Persons; Physical Exercise; Physical activity; Physiological; Physiology; Production; Recommendation; Recording of previous events; Research; Resistance; Running; Safety; Structure of beta Cell of islet; Support System; Surveys; System; Techniques; Time; Tracer; Training; United States; Variant; Walking; Weight Lifting; Work; computer framework; deep learning; design; diabetic; empowerment; exercise regimen; experience; experimental study; glucose uptake; glycemic control; human model; human study; improved; in silico; mathematical model; medical complication; model building; next generation; novel; physiologic model; predictive modeling; predictive tools; primary outcome; recruit; resistance exercise; response; safety assessment; secondary outcome; simulation; smartphone application; strength training; support tools; tool; usability

Project Summary The hallmark of type 1 diabetes (T1D) is insufficient insulin production caused by pancreatic beta cell dysfunction. Most people treat their T1D through multiple daily injections (MDI) of insulin or use of a transcutaneous insulin pump. Several decision support smartphone apps exist to help people estimate insulin doses based on continuous glucose monitor (CGM) data and food intake. More sophisticated decision support tools employ mathematical models of human physiology to predict future glucose levels and provide generalized insulin therapy recommendations. Exercise is a crucial component of the long-term management of T1D, however many people avoid physical activity for fear of hypoglycemia (< 70 mg/dL). While consensus guidelines exist to help people manage glucose during physical activity, people still experience acute complications. Mathematical models of aerobic exercise yield promise in predicting hypoglycemia during controlled in- clinic experiments but do not perform well in the real-world or during other types of exercise. There is a critical need for a decision support system that helps people with T1D maintain safe glucose levels around exercise of varying types. The goal of this proposal is to develop a decision support tool to help people with T1D who utilize CGM better manage their glucose surrounding exercise. This tool will be called AIDES, the Artificially Intelligent Diabetic Exercise Support system. We hypothesize that use of a novel exercise- specific decision support tool, powered by predictive physiological modelling, artificial intelligence (AI), and deep learning, can provide treatment recommendations to reduce the number of hypoglycemic events experienced by people with T1D around regular physical exercise. In our first aim, we will develop a new model of resistance exercise that describes both insulin- and non-insulin mediated effects on glucose dynamics. We will then create a novel hybrid computational framework that harnesses AI to augment physiology models of aerobic and resistance exercise. This hybrid framework, called physAI, will harness real- world, free-living exercise data from the T1Dexi project (Big Data). In our second aim, we will leverage decades of research into deep learning with the Big Data provided by the T1Dexi project to train an AI-based decision support system that gives treatment recommendations to help users maintain target glucose during exercise. In our third aim, we will assess the safety and usability of our decision support engine in a small proof-of-concept study with human participants, supported by the Sponsor. This will be the first decision support system specifically designed to provide treatment recommendations that help users maintain safe glucose levels while performing aerobic and resistance exercise.

项目摘要 1型糖尿病(T1D)的特点是由胰岛β细胞引起的胰岛素分泌不足 功能障碍。大多数人通过每天多次注射胰岛素(MDI)或使用 经皮胰岛素泵。有几款决策支持智能手机应用程序可以帮助人们估计胰岛素 剂量基于连续血糖监测仪(CGM)数据和食物摄入量。更复杂的决策支持 工具使用人体生理的数学模型来预测未来的血糖水平，并提供 全面性胰岛素治疗建议。锻炼是长期管理的重要组成部分 然而，对于T1D，许多人因为担心低血糖(&lt；70 mg/dL)而避免体育锻炼。在共识的同时 已有指南帮助人们在体力活动期间管理血糖，人们仍然体验到急性 并发症。有氧运动产生量预测低血糖的数学模型 受控的临床实验，但在现实世界或其他类型的运动中表现不佳。 迫切需要一种决策支持系统来帮助T1D患者维持安全的血糖水平 围绕着不同类型的运动。这项提议的目标是开发一种决策支持工具来帮助人们 对于使用CGM的T1D患者，他们更好地管理运动周围的血糖。这个工具将被称为助手， 人工智能糖尿病运动支持系统。我们假设使用一种新的练习- 特定的决策支持工具，由预测生理建模、人工智能(AI)和 深度学习，可以提供治疗建议，以减少低血糖事件的数量 患有T1D的人在定期体育锻炼前后经历的。在我们的第一个目标中，我们将开发一种新的 同时描述胰岛素和非胰岛素对血糖影响的抗阻运动模型 动力学。然后，我们将创建一种新的混合计算框架，利用人工智能来增强 有氧运动和阻力运动的生理学模型。这个名为PhyAI的混合框架将利用真正的- 来自T1Dexi项目(大数据)的世界自由生活运动数据。在我们的第二个目标中，我们将利用 利用T1Dexi项目提供的大数据对深度学习进行了数十年的研究，以培训基于AI的 决策支持系统，给出治疗建议，帮助用户在治疗期间维持目标血糖 锻炼身体。在我们的第三个目标中，我们将评估我们的决策支持引擎的安全性和可用性 与人类参与者进行的概念验证研究，由赞助商提供支持。这将是第一个决定 专门设计的支持系统，旨在提供治疗建议，帮助用户保持安全 进行有氧运动和阻力运动时的血糖水平。