一个基于Lakehouse的多源异质医疗数据增强框架
Ming Sheng1, Shuliang Wang1, Yong Zhang2
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China.
Health information science and systems
|July 8, 2024
概括
这项研究引入了一个新的医疗数据数据增强框架,集成提取,清洁和归算. 该框架提高了数据的准确性,一致性和完整性,以便更好地进行健康分析.
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
- 机器学习 机器学习
背景情况:
- 高质量的数据集对于医学数据分析至关重要.
- 现有的研究往往单独处理数据提取,清理和归算.
- 缺乏综合框架阻碍了医疗数据的准确性,一致性和完整性.
研究的目的:
- 为多源异质医疗数据提出一个新的,综合的数据增强框架.
- 解决医疗领域当前数据预处理技术的局限性.
主要方法:
- 开发了一个基于Lakehouse的框架 (MHDP),包括数据提取,清理和归算.
- 采用数据融合技术用于多模式和多源数据提取.
- 引入了HoloCleanX用于交互式数据清理,并利用多重推算 (MI) 和SAITS用于数据推算.
主要成果:
- 人类健康发展计划框架在增强医疗数据集方面表现出有效性.
- 在集群,分类和战略预测任务中评估绩效.
- 实验结果证实了该框架能够提高数据质量.
结论:
- 拟议的MHDP框架为医疗数据增强提供了有效的解决方案.
- 数据提取,清理和归算的整合大大提高了数据集的质量.
- 该框架通过改进的数据促进了更可靠的健康状况分析.
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