在临床数据仓库中追踪来源,用于质量管理
Marco Johns1, Lena Baum1, Fabian Prasser1
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Sciences, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany.
International journal of medical informatics
|November 6, 2024
概括
数据来源跟踪可以提高临床数据仓库的提取-转换-加载 (ETL) 过程中的质量管理. 该系统识别错误模式及其根本原因,提高数据质量和工作流的可重复性.
科学领域:
- 数据工程数据工程
- 临床信息学 临床信息学
- 软件开发 软件开发
背景情况:
- 数据来源对于数据处理中的可复制性和质量管理至关重要.
- 提取-转换-加载 (ETL) 过程对于建立临床数据仓库至关重要.
- 现有的ETL工作流程往往缺乏可靠的机制来跟踪数据谱系和质量问题.
研究的目的:
- 在ETL过程中设计和实施自动化数据来源跟踪框架.
- 开发一个系统来检测和报告ETL期间的数据质量问题.
- 创建一个仪表板以可视化来源信息,并促进错误的根本原因分析.
主要方法:
- 开发了一个基于Java的框架,使用Spring Framework自动跟踪数据流并检测ETL过程中的错误.
- 实现了一个应用程序编程接口 (API) 来存储来源数据和相关的质量问题.
- 创建了一个Grafana仪表板,用于在ETL工作流中可视化数据质量问题及其背景.
主要成果:
- 该框架被整合到生物和床边 (i2b2) ETL过程的信息学中.
- 对现实数据集的评估表明,它能够识别频繁的错误模式,并将其与特定的数据点和ETL步骤联系起来.
- 源地跟踪导致执行时间增加,其影响因发现问题的数量而异.
结论:
- 源地跟踪是ETL流程中持续质量管理的宝贵工具.
- API和仪表板有助于收集和可视化相关来源信息.
- 该系统有助于识别反复出现的问题及其根本原因,使得数据质量和ETL工作流程的有针对性的改进成为可能.
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