SPHN连接器 - 一个可扩展的管道,用于从联合和语义上丰富的健康数据中生成验证的知识图
Vasundra Touré1, Deepak Unni1, Philip Krauss2
1Swiss Personalized Health Network, SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
BMC medical informatics and decision making
|February 12, 2026
概括
该SPHN连接器使机构能够从各种健康数据中创建本地知识图,确保语义互操作性和数据治理. 这便于为先进的生物医学研究提供联合数据集成.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 整合异构的健康数据 (临床,队列,OMIC) 对于生物医学研究至关重要.
- 知识图为数据互操作性和重复使用提供语义链接.
- 瑞士个性化健康网络 (SPHN) 在国家范围内制定了FAIR数据原则的框架.
研究的目的:
- 介绍SPHN连接器战略和工具.
- 让数据提供商能够将本地数据转化为语义上丰富的知识图.
- 确保跨机构的语义互操作性,同时保持数据治理.
主要方法:
- 开发了SPHN连接器工具,用于局部知识图的构建.
- 使用RDF和语义网络标准进行数据转换.
- 实现了数据转换,去识别和验证的能力.
主要成果:
- SPHN连接器解决了联合知识图构建中的技术挑战.
- 不同的数据格式被转换成符合SPHN的语义丰富的RDF.
- 该工具支持代数据交付和验证.
结论:
- 生成的数据集可以集中集成或以联合的方式使用.
- 能够将患者特定的数据 (例如临床和omics) 连接起来.
- 便于将不同患者和机构的数据结合起来.
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