在医疗大数据时代,向高质量的真实实验室数据迈进
Sollip Kim1, Won-Ki Min1,2
1Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
将现实实验室数据集成到大数据中需要标准化术语,格式和可靠医疗人工智能的结果. 本综述探讨了从电子健康记录中创建高质量的医疗保健大数据的挑战和机遇.
科学领域:
- 医疗信息学 医疗信息学
- 大数据分析大数据分析
- 医疗保健中的人工智能
背景情况:
- 工业4.0强调大数据和医学中的AI.
- 电子健康记录 (EHR) 是主要的现实世界数据来源,但具有限制.
- 电子健康记录中的实验室数据在术语,格式和计量方面往往缺乏标准化,这阻碍了跨实验室的比较.
研究的目的:
- 审查将现实实验室数据整合到高质量的大数据中的基本组件.
- 解决标准化术语,数据格式,方程和结果协调方面的挑战.
- 讨论人工智能应用的实验室结果转移,调整和质量认证.
主要方法:
- 文献综述侧重于实验室数据的标准化和协调.
- 对医疗人工智能的现实世界数据集成挑战的分析.
- 对计量学,术语和数据格式标准化的讨论.
主要成果:
- 术语,数据格式和计量学的标准化对于实验室数据质量至关重要.
- 结果的协调和标准化是实现可靠的大数据分析的关键.
- 处理数据传输,调整和质量认证对于医疗保健AI至关重要.
结论:
- 用人工智能来利用现实世界的实验室数据需要强大的标准化策略.
- 克服数据异质性对于在医疗大数据中利用电子健康记录至关重要.
- 标准化,高质量的实验室数据为先进的医疗保健人工智能应用铺平了道路.
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