确定处理临床结构化数据集中缺失值的最合适的归算方法:系统性审查.
Marziyeh Afkanpour1, Elham Hosseinzadeh1, Hamed Tabesh2
1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
BMC medical research methodology
|August 28, 2024
概括
了解缺失的数据是临床研究的关键. 本综述根据数据特征绘制了归算技术,以指导医疗分析师选择可靠结果的最佳方法.
科学领域:
- 数据科学数据科学数据科学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 准确处理临床数据集中缺失的值对于可靠的研究结果和知情决策至关重要.
- 随着数据复杂性的增加,需要有效的归算技术来解决缺失的数据.
- 本研究的重点是指导健康分析师根据数据集特征选择合适的归算方法.
研究的目的:
- 在临床数据集中系统地审查和引入各种缺失值的归算技术.
- 开发一个证据地图,根据缺失数据的机制,模式和比率推合适的归算方法.
- 为在结构化临床数据集的数据预处理过程中选择适当的归算方法提供准则.
主要方法:
- 在PubMed,科学网,Scopus和IEEE Xplore上进行系统的文献搜索,截至2023年9月20日.
- 分析的重点是缺失的数据机制,模式,比率和归算策略.
- 综合洞察力,构建一个证据地图,推归算方法.
主要成果:
- 在2955篇文章中,包括58篇在分析中.
- 在45%的研究中使用了传统的统计方法.
- 31%的研究采用了机器学习/深度学习方法,24%的研究采用了混合技术.
结论:
- 根据缺失的数据特征选择归算技术对于临床数据集至关重要.
- 准确的归算提高了数据质量,可重复使用性,并支持精确的医疗决策.
- 本综述为在数据预处理阶段选择最佳归算方法提供了准则.
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