在OntoBridge与传统ETL相比:在DATOS-CAT项目中使用本体学来加强数据标准化到CDM格式
Guillem Bracons Cucó1,2, Jessyca Gil Rojas1, Petter Peñafiel Macias3
1Clinical Informatics Service, Hospital Clínic de Barcelona. 08036 - Barcelona, Spain.
Studies in health technology and informatics
|August 23, 2024
概括
基于本体学的工具OntoBridge简化了将本地数据转换为通用数据模型 (CDM). 它比传统的ETL方法提供了更大的灵活性和可扩展性,用于数据集成和管理各种CDM.
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
- 健康数据管理 管理健康数据
背景情况:
- 共同数据模型 (CDM) 对于数据交换和集成至关重要,但转换本地数据是资源密集的.
- 当前的方法在适应新数据源或更新模型时,往往缺乏灵活性和可扩展性.
- 观察医学结果伙伴关系 (OMOP) CDM被广泛采用,但工具往往是OMOP特定的.
研究的目的:
- 将基于本体学的OntoBridge工具与用于将本地数据集转换为CDM的传统ETL方法进行比较.
- 在管理数据源,CDM更新和采用新的CDM时评估灵活性,可扩展性和可重复使用性.
- 在OMOP CDM采用的背景下,评估OntoBridge与传统ETL的效率.
主要方法:
- 对 OntoBridge 和 CDM 转换的传统 ETL 流程进行比较分析.
- 对新数据源和共同数据模型 (CDM) 更新的适应性评估.
- 评估采用各种CDM的可扩展性,包括OMOP和i2b2.2.
主要成果:
- 在整合新数据源和适应共同数据模型 (CDM) 更新方面,OntoBridge表现出卓越的灵活性.
- OntoBridge的可扩展性更强,可以采用各种CDM,如i2b2.2.
- 传统的ETL方法显示出局限性,通常依赖于OMOP特定的工具和OHDSI开发.
结论:
- 在OntoBridge提供了一个更灵活,可扩展和维护效率高的替代传统的ETL用于共同数据模型 (CDM) 数据集成.
- 基于本体学的方法可以提高数据转换过程的适应性和可重复使用性.
- 这些发现表明,OntoBridge可以促进各种CDM在OMOP之外的更广泛采用.
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