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Statistical Problems in Closed-Loop Diabetes Control

Statistical Problems in Closed-Loop Diabetes Control
闭环糖尿病控制中的统计问题
批准号:
1106753
负责人:
Cun-Hui Zhang
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-15 至 2015-07-31

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中文摘要
翻译
该项目的重点是开发闭环糖尿病控制的统计模型,方法和相关理论。具有闭环胰岛素输送系统的人工胰腺仍处于开发的早期阶段,预计将彻底改变糖尿病的治疗方式。正如PI和许多其他人所认识到的那样,开发人工胰腺的主要障碍是葡萄糖传感器的不可靠性。传感器技术并不那么新,而且也非常聪明,但由于传感器工作方式的物理过程,需要经常重新校准。拟议的项目将通过对所涉及的代谢过程进行适当的物理建模来解决适当重新校准的问题。 传感器全天候以固定的时间增量测量间隙(皮下脂肪)中的电流。电流名义上与空间中的葡萄糖量成比例,但测量存在两个问题。首先,葡萄糖从血流扩散到脂肪中存在延迟,使得脂肪中的葡萄糖密度滞后于血流中的葡萄糖密度。其次,存在一种防御机制(白色血细胞),其作为异物包围电极并试图通过生物污垢将其去除。除非进行精确的重新校准,否则白色血细胞会干扰电流并产生错误的测量结果。所提出的方法还没有尝试过,预计将显着优于目前的传感器技术的实施方式的基础上更直接的回归,而不获得的好处和洞察力的物理建模。采用微分方程方法处理葡萄糖传感器的延迟问题。微分方程被广泛接受,葡萄糖从血液扩散到组织间隙的速率决定了血流和组织间隙中葡萄糖密度之间的关系。扩散速率和生物污垢的影响将通过每天几次的手指针刺计量测量来估计。所提出的方法的一个主要创新是使用延迟和生物污染问题的物理学的统计模型。对于数百万每天24小时都要面对必须决定何时和注射多少胰岛素的单调乏味的美国人来说,人造胰腺将是天赐之物。如果这可以为他们自动完成,那么他们将继续拥有的唯一任务就是记住更换胰岛素泵井中的胰岛素。人工胰腺将为他们的疾病提供有效的治疗。
英文摘要
The project focuses on developing statistical models, methods and related theory for closed-loop diabetes control. An artificial pancreas with a closed-loop insulin delivery system, still in an early stage of its development, is expected to revolutionize the way diabetes is treated. As the PIs and many others recognized, a major impediment to the goal of developing an artificial pancreas is the unreliability of the glucose sensor. Sensor technology is not so new and it is also remarkably clever, but frequent recalibration is needed because of the physical processes underlying the way sensors work. The proposed project will solve the problem of proper recalibration by appropriate physical modeling of the metabolic processes involved. Sensors measure current in interstitial space (subcutaneous fat) at fixed time increments all around the clock. The current is nominally proportional to the amount of glucose in the space, but there are two problems with the measurements. Firstly, there is a delay in diffusion of glucose from the bloodstream into fat so that the glucose density in the fat lags the glucose density in the bloodstream. Secondly, there is a defense mechanism (white blood cells) which surround the electrode as a foreign body and attempt to get rid of it via biofouling. The white blood cells interfere with current flow and produce erroneous measurements unless accurate recalibration is performed. The proposed approach has not been tried and is expected to significantly outperform the current implementation of the sensor technology based on more straightforward regression without gaining the benefit and insight of the proposed physical modeling. A new differential equation approach will be used to deal with the delay problem related to glucose sensor. The differential equation is widely accepted and the rate for the diffusion of glucose from the blood into interstitial space governs the relationship between the glucose densities in the bloodstream and interstitial spaces. The diffusion rate and the effect of biofouling will be estimated from finger stick metered measurements taken a few times a day. A main innovation in the proposed approach is statistical models using the physics of the delay and biofouling problems.An artificial pancreas will be a godsend to the millions of Americans faced with the 24 hour a day tedium of having to decide when and how much insulin to inject. If this could be done automatically for them then the only task they would continue to have is to remember to replace insulin in the well of their insulin pump. An artificial pancreas would give them an effective treatment for their disease.
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Estimation and Inference with High-Dimensional Data
  • 批准号:
    2210850
  • 项目类别:
    Standard Grant
  • 资助金额:
    $29.0万
  • 财政年份:
    2022
  • 负责人:
    Cun-Hui Zhang
  • 依托单位:
FRG: Collaborative Research: Dynamic Tensors: Statistical Methods, Theory, and Applications
  • 批准号:
    2052949
  • 项目类别:
    Standard Grant
  • 资助金额:
    $60.0万
  • 财政年份:
    2021
  • 负责人:
    Cun-Hui Zhang
  • 依托单位:
Collaborative Research: Statistical Methods, Algorithms, and Theory for Large Tensors
  • 批准号:
    1721495
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $26.0万
  • 财政年份:
    2017
  • 负责人:
    Cun-Hui Zhang
  • 依托单位:
SEMIPARAMETRIC INFERENCE WITH HIGH-DIMENSIONAL DATA
  • 批准号:
    1513378
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2015
  • 负责人:
    Cun-Hui Zhang
  • 依托单位:
海外基金