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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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相关实验视频

Updated: Jun 9, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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在mHealth数据分析中的过程挖掘.

Michael Winter1,2, Berthold Langguth3, Winfried Schlee3,4

  • 1Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany. michael.winter@uni-wuerzburg.de.

NPJ digital medicine
|October 24, 2024
PubMed
概括
此摘要是机器生成的。

过程挖掘从移动健康数据提供了新的临床见解,补充了机器学习. 这种方法通过专注于时间模式来更好地理解健康变异性来解决健康数据分析中的挑战.

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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相关实验视频

Last Updated: Jun 9, 2025

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科学领域:

  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学
  • 临床研究 临床研究

背景情况:

  • 移动健康 (mHealth) 产生了大量复杂的健康数据.
  • 传统的数据驱动方法,如机器学习,面临诸如选择偏差和数据动态等挑战.
  • 需要先进的分析技术来充分利用mHealth数据来获得临床见解.

研究的目的:

  • 探索过程挖掘的应用,从mHealth数据中提取临床见解.
  • 展示过程挖掘如何可以补充现有的数据驱动技术.
  • 突出过程挖掘在解决健康数据分析方面的挑战方面的潜力.

主要方法:

  • 过程挖掘技术用于分析mHealth数据中的时间过程模式.
  • 过程挖掘与机器学习等数据驱动技术的比较.
  • 通过过程分析,专注于识别和理解健康状况的变化.

主要成果:

  • 过程挖掘可以有效地从mHealth数据集中提取有意义的临床见解.
  • 它为机器学习提供了互补的视角,特别是在理解过程动态方面.
  • 该方法为健康状况的变化提供了宝贵的见解.

结论:

  • 过程挖掘对推进移动健康数据分析具有重大潜力.
  • 它提供了一种强大的方法来补充机器学习和解决数据复杂性.
  • 在医疗保健分析中进一步探索过程挖掘是有必要的.