一个基于知识图的数据协调框架,用于二次数据的再利用
Francisco Abad-Navarro1, Catalina Martínez-Costa1
1Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, 30100, Murcia, Spain.
Computer methods and programs in biomedicine
|November 19, 2023
概括
本研究引入了一个语义驱动的框架,用于协调异质的医疗保健数据,使跨机构的数据共享和分析能够用于机器学习应用.
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
- 本体学 工程学 工程学
背景情况:
- 临床护理系统产生大量的异质数据.
- 数据协调对于整合和分析这些有价值的信息至关重要.
研究的目的:
- 提出医疗保健数据的语义驱动的协调框架.
- 实现跨机构数据的有意义的共享和整合.
- 为了促进先进的数据分析和利用.
主要方法:
- 开发了一个基于本体学的常用数据模型 (SCDM).
- 实现了一个数据转换管道和一个语义查询系统.
- 利用基于本体学的基础架构和图形数据库进行集成.
主要成果:
- 在Precise4Q项目中,成功地整合了来自多个欧洲机构的异质数据集.
- 使数据科学家能够通过语义查询系统探索集成的数据.
- 促进了统一数据的使用,用于构建机器学习模型.
结论:
- 语义共同数据模型 (SCDM) 和RDF使异质医疗数据的语义整合成为可能.
- 该框架支持用于研究和临床应用的先进数据利用.
- 在欧洲H2020 Precise4Q项目中证明了成功的应用.
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