A Run-to-Run Algorithm for Glucose Regulation

葡萄糖调节的逐次运行算法

• 批准号: 6953163
• 负责人: LOIS JOVANOVIC
• 金额: $ 20.24万
• 依托单位: SANSUM DIABETES RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6953163
• 关键词: artificial intelligence; bioengineering /biomedical engineering; biomedical automation; blood glucose; clinical research; clinical trials; computer assisted patient care; computer simulation; dietary carbohydrates; drug administration rate /duration; drug delivery systems; glucose metabolism; human subject; insulin dependent diabetes mellitus; insulin sensitivity /resistance; mathematical model; model design /development; patient oriented research

DESCRIPTION (provided by applicant): The long term objective of this work is to develop new algorithmic approaches to optimize the delivery of insulin in an automated fashion to people with type 1 diabetes. Specifically, we aim to develop a strategy, inspired by run-to-run control theory established by the chemical process industries, that "learns" from the previous sequence of glucose responses to insulin dosing (over the course of days), and optimally predicts the appropriate strategy for the forthcoming day. The notion of a "cycle" in engineering will be extended to manage the 24 hour routine of repeated meals, activities, and sleep cycles and the corresponding dosing of insulin. The algorithm will be tested in both simulation and clinical trials for robustness to sensor noise, uncertainty in the patient characterization, variability in the timing of the postprandial glucose peak, and variability in the carbohydrate content in the meals. The Specific Aims of this project are to: i) construct predictive patient sensitivity models for calculation of optimal insulin dosing from elevated (or depressed) glucose levels, ii) develop run-to-run algorithm for insulin bolus dosing to provide corrections in subsequent days based on previous history of glucose levels and insulin dosage, and iii) evaluate the robustness of the algorithm through meal challenges of varying carbohydrate content. The aims will blend prototype algorithms that are drawn from systems engineering with validation in a series of clinical tests. The proposed collaboration between systems engineers and renowned diabetes researchers in an established clinical research setting will allow a novel fusion of methods that can be truly characterized as "innovative". The medical collaborators in the proposal are located at the prestigious Sansum Medical Research Institute, which is located less than 10 miles from the campus of the University of California, Santa Barbara. The exchange of personnel will be facilitated, allowing the student and post-doc supported on this project to work at both the institute and the university over the span of the project

描述（由申请人提供）： 这项工作的长期目标是开发新的算法方法，以自动化胰岛素的方式优化1型糖尿病患者的胰岛素。具体而言，我们旨在制定一种策略，灵感受到化学过程行业建立的运行控制理论的启发，该策略从先前的葡萄糖反应序列（在几天内）“学习”，并最佳预测即将到来的一天的适当策略。工程中“周期”的概念将扩展，以管理重复的饭菜，活动和睡眠周期的24小时例行程序以及胰岛素的相应剂量。该算法将在模拟和临床试验中测试传感器噪声的鲁棒性，患者表征的不确定性，餐后葡萄糖峰时间的变化以及餐食中碳水化合物含量的可变性。该项目的具体目的是：i）构建预测性的患者敏感性模型，以计算升高（或抑郁症）葡萄糖水平的最佳胰岛素剂量，ii）ii）开发胰岛素推注的持续运行算法，以根据先前的葡萄糖水平和挑战的速度和胰岛素剂量的范围来提供胰岛素剂量以在随后的几天内提供纠正，并构建胰岛素剂量的校正​​。碳水化合物含量。目的将在一系列临床测试中融合由系统工程绘制的原型算法和验证。在既定的临床研究环境中，系统工程师与著名的糖尿病研究人员之间提议的合作将允许一种新颖的方法融合，这些方法可以真正被描述为“创新”。该提案中的医疗合作者位于著名的Sansum医学研究所，该研究所距离加利福尼亚大学圣塔芭芭拉分校的校园不到10英里。将促进人员交流，使该项目支持该项目的学生和DOC在该项目的范围内在研究所和大学工作