包含个人健康信息的自由文本数据的非识别:对审查的范围审查
Bekelu Negash1, Alan Katz1,2, Christine J Neilson3
1Manitoba Centre for Health Policy, Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba.
International journal of population data science
|February 28, 2024
概括
在研究中去识别自由文本数据是复杂的. 本综述对基于规则的方法,机器学习和深度学习等方法进行了分类,发现混合策略对保护健康信息最有希望.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 自然语言处理自然语言处理.
背景情况:
- 不识别健康数据对于研究隐私至关重要,特别是对于非结构化的自由文本.
- 现有的结构化数据方法对于复杂的临床笔记和电子健康记录是不够的.
- 科学文献中记录了一些免费文本数据的非识别技术.
研究的目的:
- 进行对用于去识别自由文本数据的方法的范围审查.
- 识别和分类现有的非结构化健康信息的非识别方法.
- 了解自由文本非识别的演变和未来趋势.
主要方法:
- 采用了一种系统的范围审查方法.
- 在Ovid MEDLINE,Embase,Scopus,ACM数字图书馆,IEEE Explore和Compendex进行了搜索,截至2022年5月.
- 两个独立的审稿人使用Covidence.com对标题,摘要和全文进行了选.
主要成果:
- 搜索结果为3312篇文章;其中18篇符合自由文本非识别方法的纳入标准.
- 大多数方法集中在删除HIPAA定义的个人健康识别信息 (PHII) 类别上.
- 通常描述的方法涉及基于规则的系统,机器学习和深度学习的组合.
结论:
- 自由文本数据的非识别方法分为基于规则的,机器学习,深度学习和混合方法.
- 这些方法的演变显示出一种趋向于更复杂的,结合策略的趋势.
- 综合多种技术的混合方法被认为是未来对自由文本数据的非识别最有希望的方法.
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