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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
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糖尿病:使用生物传感器信号的联合学习进行非侵入性血糖监测.

Narmatha Chellamani1, Saleh Ali Albelwi1, Manimurugan Shanmuganathan1

  • 1Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia.

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概括

这项研究引入了一种联合学习方法,用于使用光聚光学信号进行非侵入性血糖监测. 该方法确保数据隐私,同时实现精确的葡萄糖水平预测,优于传统的深度学习模型.

关键词:
克拉克错误网格分析深度神经网络 (DNN) 是一个深度神经网络.糖尿病管理 糖尿病管理联合学习 (FL)医疗保健 医疗保健 医疗保健 医疗保健机器学习是机器学习.非侵入性血糖监测是指非侵入性的血糖监测.摄影复发性脑膜成像 (PPG)

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 糖尿病管理需要精确的血糖水平 (BGL) 监测,以防止严重的并发症.
  • 传统的BGL监测方法 (例如,指针测试) 由于侵入性,患者的坚持率较低.
  • 非侵袭性BGL监测技术对于改善患者遵守和长期健康结果至关重要.

研究的目的:

  • 开发一种保护隐私的非侵入性方法,用于使用光聚光显微镜 (PPG) 信号监测血糖水平 (BGL).
  • 利用联合学习 (FL) 在多个机构之间进行深度神经网络 (DNN) 的协作培训,而无需共享数据.
  • 通过先进的信号处理和特征选择,提高非侵入性BGL预测的准确性和临床可靠性.

主要方法:

  • 用连续波形变换 (CWT) 进行预处理,以减少噪声和基于自适应周期的细分 (ACBS).
  • 粒子群优化 (PSO) 用于特征选择,以提高分类准确性.
  • 一个联合学习 (FL) 框架使深度神经网络 (DNN) 在来自多个医疗保健组织的分布式数据集上进行协作训练.

主要成果:

  • 拟议的基于FL的系统在各种数据集 (VitalDB,MUST) 上实现了19.1 mg/dL的根平均平方误差 (RMSE).
  • 克拉克错误网格分析 (CEGA) 显示出高临床可靠性,99.31%的预测在可接受范围内.
  • 联合学习方法在预测准确性方面明显优于传统的集中式深度学习模型.

结论:

  • 联合学习为保护隐私的非侵入性葡萄糖监测模型的协作培训提供了强大的解决方案.
  • 将CWT,ACBS和PSO与FL集成,提高了基于PPG的BGL预测的准确性和临床实用性.
  • 这种方法代表了在糖尿病管理中广泛采用非侵入性葡萄糖监测的重大进展.