加强人工智能研究的临床数据基础设施:数据管理架构的比较评估
Richard Gebler1, Ines Reinecke2, Martin Sedlmayr1
1Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.
Journal of medical Internet research
|August 1, 2025
概括
选择正确的临床数据管理架构是人工智能研究和患者护理的关键. 数据仓库提供治理,数据湖提供灵活性,数据湖平衡两者,但需要专业知识.
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
- 医疗保健中的人工智能
背景情况:
- 临床数据的快速增长给医疗机构带来了挑战.
- 传统的数据管理难以处理大型,多样化和动态的数据集.
- 需要支持人工智能研究和改善患者护理的架构.
研究的目的:
- 比较临床数据仓库,数据湖和数据湖仓库.
- 使用FAIR原则和大数据5V分析架构.
- 指南选择平衡数据治理和分析灵活性.
主要方法:
- 开发了一个整合数据治理和技术性能的框架.
- 进行了关于数据管理架构的快速文献审查.
- 评估了可扩展性,实时处理,元数据和专业知识要求.
主要成果:
- 数据仓库:强有力的治理,有限的可扩展性/实时处理.
- 数据湖:灵活,可用于异质数据的可扩展,潜在的质量/元数据问题.
- 数据湖房:结合优势,需要高专业知识和复杂的集成.
结论:
- 最佳的架构取决于组织的需求,资源和目标.
- 为未来的基础设施平衡治理,灵活性和可扩展性.
- 需要进一步的研究来简化混合模型并改善临床标准的整合.
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