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A User-friendly and Powerful R Analysis of Large-scale Datasets
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实验室单元在大型临床数据中的自动标准化和协调,使用开源R函数
Ahmed Medhat Zayed1, Ilias Sarikakis2, Nicolas Delvaux3
1Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium; Laboratory Medicine Department, Menoufia University National Liver Institute, Shebin El-Kom, Egypt.
International journal of medical informatics
|October 8, 2025
概括
自动化功能标准化和协调临床实验室单元,显著提高大数据集的数据一致性和互操作性. 这增强了用于分析和机器学习应用程序的数据集成.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床数据科学 临床数据科学
- 健康数据标准化 标准化
背景情况:
- 临床实验室数据的二次使用受到电子医疗记录中不一致的单位表示的阻碍.
- 异质的多源数据库遭受非标准的单元字符串和碎片化的映射,限制互操作性和引入偏差.
- 像UCUM和LOINC这样的现有标准在现实数据应用中面临着挑战.
研究的目的:
- 开发和评估开源功能,用于将实验室单元自动化标准化为UCUM有效格式.
- 为了提高数据的一致性,将实验室结果与LOINC组内的参考单位协调一致.
- 加强大规模临床实验室数据的二次使用和互操作性.
主要方法:
- 开发了两个可扩展的开源R函数,用于单位标准化和价值协调.
- 标准化采用预处理,确定性映射和基于规则的对异质单位表达式进行校正.
- 应用函数对1639万个LOINC映射的定量结果;验证使用了UCUM网络服务检查.
主要成果:
- 成功标准化了1.639亿条记录中的96.2%,将单位异质性降低了81% (UCUM单位从2,019到381).
- 协调在几分钟内将83.4%的记录转换为参考单位,与UCUM API输出100%一致.
- 协调纠正了280万条记录中的LOINC财产不一致性,并改善了23%的LOINC集团的分布特征.
结论:
- 实验室单元的自动化,可扩展的标准化和协调是可行的,对于大型多源数据集至关重要.
- 这些功能使得可靠的数据集成用于分析和机器学习,减少偏差从单元变化.
- 开源实现方便了常规ETL管道集成,推进了现实世界实验室数据的再利用.
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