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
概括
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.
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.
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