正常的工作流程和关键数据清理策略向现实世界的数据:观点
Manping Guo1,2,3, Yiming Wang3, Qiaoning Yang3
1Postdoctoral Research Station, China Academy of Chinese Medical Sciences, Beijing, China.
Interactive journal of medical research
|September 21, 2023
概括
这项研究引入了一个数据清理框架,以解决医学研究中的"数据灾难". 它提供了一种工作流和指导,用于处理重复,缺失和异常数据,以提高数据质量.
科学领域:
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
- 研究方法研究方法研究方法学
背景情况:
- 快速的科学进步产生了大量的真实世界数据,导致了
- 数据灾难 数据灾难
- 特别是在医学研究中.
研究的目的:
- 为现实研究提出一个数据清理框架.
- 提供有关高效和道德数据清理流程的指导.
- 解决重复,缺失和异常数据的常见问题.
主要方法:
- 开发一个针对现实世界研究的数据清理框架.
- 识别并专注于三种最常见的脏数据类型:重复,缺失和异常数据.
- 建立数据清理程序的标准工作流程.
主要成果:
- 一个全面的数据清理框架,专为现实研究环境而设计.
- 一个结构化的工作流来指导数据清理流程.
- 为克服在数据清理过程中遇到的常见挑战提供实用建议.
结论:
- 有效的数据清理对于医疗研究中可靠的数据分析和管理至关重要.
- 拟议的框架和工作流程为改善数据质量提供了有价值的指导.
- 解决重复,缺失和异常数据对于强大的现实世界证据生成至关重要.
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