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Multiple disturbance modeling and prediction of blood glucose in Type 1 Diabetes Mellitus.

机译：1型糖尿病的多重干扰建模和血糖预测。

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摘要

Type 1 diabetics often experience extreme variations in glucose concentrations which can have adverse long- and short-term effects such as severe hypoglycemia, hyperglycemia and organ degeneration. Studies have established that there is a need to maintain the glucose levels within a normal range (e.g. 80-- 150 mg/dL) to avoid complications caused by diabetes. However, initial attempts to regulate blood glucose levels using insulin infusion pumps, multiple injections or a combination of the two have had limited success as they lack the ability to decide the appropriate rate and/or dose of insulin based on the current metabolic state of the body. Consequently, what is needed is an automatic insulin delivery system (i.e., artificial pancreas) with the ability to determine continuously the rate of insulin delivery required to provide optimum closed-loop glucose control (i.e., to minimize the variability around a desired glucose level) and to eliminate the individual from the insulin dosage decision making in this control loop. Due to recent advances in biomedical technology, such as automatic insulin delivery systems using glucose sensors and insulin pumps, blood glucose modeling and control has received considerable attention in the process control community and models of various degrees of complexity have been developed. Glucose levels are affected by many variables, such as stress, physical activity, hormonal changes, periods of growth, medications, illness/infection, fatigue, as well as food intake and insulin tolerance. Furthermore, not only does glucose change from several sources of disturbances but their impact on blood glucose level is highly correlated, dynamic and nonlinear making it difficult to distinguish the effect each input has on blood glucose. Thus, the objective of this research is to introduce a modeling methodology that is able to take into account the simultaneous and multiple effects of food, activity, stress and their interactions.;The research presented in this thesis is carried out on 15 Type 1 diabetic subjects where thirteen variables (i.e., three food variables, seven activity variables, basal insulin, bolus insulin, and time of day (TOD)) are collected for two weeks and modeled using the Wiener block-oriented model. Three types of models are compared: input-only (Model 1), input-output (Model 2), and output-only (Model 3). Results are given for k-steps ahead prediction (k-SAP) from 5 minutes to 3 hours in the future and show the importance of taking into account the interactions between input variables.
机译：1型糖尿病患者的血糖浓度经常出现极端变化，这可能会产生不利的长期和短期影响，例如严重的低血糖，高血糖和器官变性。研究表明，有必要将葡萄糖水平维持在正常范围内（例如80-150 mg / dL），以避免糖尿病引起的并发症。然而，最初尝试使用胰岛素输注泵，多次注射或两者结合来调节血糖水平的尝试取得了有限的成功，因为它们缺乏根据胰岛素的当前代谢状态决定合适的胰岛素剂量和/或剂量的能力。身体。因此，需要一种能够连续确定提供最佳闭环葡萄糖控制（即，将所需葡萄糖水平附近的可变性最小化）所需的胰岛素递送速率的能力的自动胰岛素递送系统（即人造胰腺）。并在此控制回路中将个体从胰岛素剂量决策中剔除。由于生物医学技术的最新进展，例如使用葡萄糖传感器和胰岛素泵的自动胰岛素输送系统，血糖建模和控制在过程控制领域引起了相当大的关注，并且已经开发了各种复杂程度的模型。葡萄糖水平受许多变量的影响，例如压力，身体活动，荷尔蒙变化，生长周期，药物治疗，疾病/感染，疲劳以及食物摄入和胰岛素耐受性。此外，葡萄糖不仅会从多种干扰源发生变化，而且它们对血糖水平的影响是高度相关的，动态的和非线性的，这使得难以区分每个输入对血糖的影响。因此，本研究的目的是介绍一种建模方法，该模型能够考虑食物，活动，压力及其相互作用的同时和多重影响。本论文中的研究是针对15个1型糖尿病患者进行的。在受试者中收集了13个变量（即，三个食物变量，七个活动变量，基础胰岛素，推注胰岛素和一天中的时间（TOD）），为期两周，并使用面向维纳块的模型进行建模。比较了三种类型的模型：仅输入（模型1），输入-输出（模型2）和仅输出（模型3）。给出了从5分钟到3小时的k步提前预测（k-SAP）的结果，并显示了考虑输入变量之间的相互作用的重要性。

著录项

  • 作者

    Kotz, Kaylee Renee.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Engineering Chemical.;Engineering Biomedical.
  • 学位 M.S.
  • 年度 2011
  • 页码 91 p.
  • 总页数 91
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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