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在长度儿童生长数据中检测异常测量和轨迹的新方法和技术考虑
Paraskevi Massara1, Arooj Asrar2, Celine Bourdon3
1Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada. p.massara@mail.utoronto.ca.
BMC medical research methodology
|October 13, 2023
概括
准确的生长研究需要强大的异常值检测. 这项研究评估了识别测量和轨迹异常值的方法,发现基于集群的轨迹检测最有效,并突出了对增长模式分析的重大影响.
科学领域:
- 儿科 儿科 儿科
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 纵向生长研究依赖于轨迹数据,但人体测量测量容易出现产生异常值的错误.
- 目前用于检测异常测量的方法缺乏黄金标准,对于异常轨迹没有确立的方法.
- 调查异常类型及其对增长模式检测的影响对于准确的分析至关重要.
研究的目的:
- 评估六种方法在检测增长数据中的各种异常类型和强度方面的表现.
- 提出和评估两个新的方法来检测异常轨迹.
- 为了确定异常值对增长模式检测的影响.
主要方法:
- 包括393名健康婴儿和1651名严重营养不良的儿童.
- 注入了三种类型和六种强度的异常值;应用了四种测量异常值检测方法 (基于模型的,WHO截止值) 和两种轨迹方法.
- 使用时间序列聚类和隐性类混合模型在异常注入前后评估增长模式检测.
主要成果:
- 基于模型的测量异常值检测显示高精度 (5.72-99.89%),特别是在低至中等误差强度.
- 基于集群的异常值轨迹方法在所有错误类型和强度中显示出高精度 (14.93-99.12%).
- 异常值将增长群成员数量改变了57.9%至79.04%,强调了它们的重大影响.
结论:
- 基于模型的方法在中度,低强度的错误中表现出色,而世卫组织的切断方法适用于极端,高强度的错误.
- 基于集群的异常值轨迹检测提供了一个强大的方法来处理增长数据中的各种错误.
- 有效的异常值检测对于可靠的儿童生长研究至关重要,因为异常值可以大幅扭曲生长模式分析.
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