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2SpamH:一种双阶段预处理算法,用于被动感应的mHealth数据.

Hongzhe Zhang1, Jihui L Diaz1, Soohyun Kim1

  • 1Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA.

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本研究引入了一种新的算法,通过评估设备使用情况来提高移动健康 (mHealth) 数据的准确性. 2SpamH算法有助于纠正被动感知数据中的偏差,以获得更好的数字健康洞察力.

关键词:
k-最近的邻居算法算法移动健康的移动健康被动感应是一种被动感应.智能手机的智能手机智能手机的智能手机.

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科学领域:

  • 数字健康数字健康
  • 移动健康 (mHealth) 服务提供商
  • 生物医学数据科学 生物医学数据科学

背景情况:

  • 移动健康 (mHealth) 技术和可穿戴设备越来越多地用于收集个性化的行为数据.
  • 用户和时间之间设备使用的变化可能导致被动感知数据的低估和偏差.
  • 处理数据质量对于数字健康应用程序的准确分析至关重要.

研究的目的:

  • 提出一种无监督的算法,即被动感应的mHealth数据 (2SpamH) 的二阶段预处理算法,以推断被动感应数据的质量.
  • 为了解决异质设备使用在mHealth数据中引入的偏差.
  • 与现有方法相比,评估算法的实用性.

主要方法:

  • 开发了无监督的2SpamH算法.
  • 使用设备使用变量来推断被动感知mHealth数据的质量.
  • 进行模拟研究并将算法应用于真实临床数据集.

主要成果:

  • 拟议的2SpamH算法有效地推断出移动设备的被动传感数据的质量.
  • 与现有方法相比,模拟研究表明2SpamH的实用性.
  • 将算法成功应用于现实世界的临床数据集.

结论:

  • 2SpamH算法提供了一个强大的解决方案,用于提高被动感知mHealth数据的质量.
  • 这种方法可以减轻因不同设备使用模式造成的偏差.
  • 这些发现支持将这些算法集成到更可靠的数字健康研究和应用中.