基于语义的电子健康记录数据质量的改进:系统性审查
Yirong Wu1, Mudan Ren2, Na Chen2
1Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, 519087, China.
BMC medical informatics and decision making
|August 12, 2025
概括
语义技术可以在各种应用中显著提高电子健康记录 (EHR) 数据质量. 六种关键技术,包括本体学和知识图,提高了EHR数据质量,而不仅仅是互操作性.
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
- 语义网络技术 语义网络技术
背景情况:
- 电子健康记录 (EHR) 数据质量对于健康信息化至关重要,但仍然是一个挑战.
- 语义技术提供了一个有希望的方法来提高电子健康记录数据的质量.
- 以前的研究往往只集中在特定的技术或应用程序上,例如互操作性.
研究的目的:
- 系统地审查各种语义技术的潜力,以提高EHR数据质量.
- 探索这些技术更广泛的应用场景.
主要方法:
- 按照PRISMA指南进行系统审查.
- 搜索了PubMed,IEEE Xplore和科学网络核心集合.
- 使用的关键词包括"语义"",质量"",电子健康记录"",EHR"",电子医疗记录"和EMR.
主要成果:
- 分析了2008年至2024年间发表的37篇论文.
- 确定了六种关键的语义技术:电子语文标准化,控制词汇,本体学,语义网络,知识图和NLP.
- 将这些技术与16个数据质量指标和FAIR原则进行映射,显示对技术和治理方面的贡献.
结论:
- 六种语义技术 (基础,一般,高级) 可以提高电子健康记录数据的质量.
- 这些技术提高了符合性,可移植性,可用性和适用性.
- 它们适用于超越互操作性的各种环境,支持FAIR数据管理和重用.
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