使用R和RStudio进行EHR实验室数据预处理和季节调整的协议
Victorine P Muse1, Søren Brunak2
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
STAR protocols
|March 1, 2024
概括
实验室数据中的季节性可能会扭曲诊断. 该协议使用R软件来分析电子健康记录的季节性模式,并调整临床参考间隔,提高诊断准确度.
科学领域:
- 临床实验室科学 临床实验室科学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 实验室医疗保健数据显示季节性变化,可能导致患者诊断不足或过度.
- 准确的诊断参考间隔对于有效的临床决策至关重要.
研究的目的:
- 提出一种可复制的协议,用于分析实验室医疗保健数据中的季节性.
- 根据已识别的季节性模式,提供调整现有参考区间的方法.
- 利用电子健康记录 (EHR) 数据进行可靠的季节性分析.
主要方法:
- 预处理全人口患者实验室数据到统一的数据集.
- 对季节性分析的相关层次的定义.
- 用R软件将数据规范化到中位数,并适应选定的统计函数.
主要成果:
- 该协议允许在实验室测试结果中识别和量化季节性趋势.
- 详细说明了调整参考间隔以考虑季节性的方法.
- 这种方法有助于在一年中更准确地解释实验室数据.
结论:
- 分析实验室数据的季节性对于减轻诊断错误至关重要.
- 这种基于R的协议提供了一种标准化的方法来调整参考间隔,提高诊断精度.
- 实施这些调整可以通过确保更准确的诊断来改善患者护理.
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