临床数据的非识别:对自由文本,图像和表格数据方法的系统审查
Pedro Faustini1, Annabelle McIver1, Ryan Sullivan2
1School of Computing, Macquarie University, Balaclava Rd, Sydney, 2109, NSW, Australia.
International journal of medical informatics
|December 27, 2025
概括
使用自然语言处理 (NLP) 进行医疗数据的自动化非识别对于隐私至关重要. 最近的进展表明,语言模型的使用越来越多,但处理多种语言和数据稀缺仍然存在挑战.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健数字化产生敏感数据,需要对隐私和合规性进行非识别.
- 自然语言处理 (NLP) 提供了用于消除医疗数据身份的自动化解决方案.
研究的目的:
- 在过去的十年里,系统地审查基于NLP的医疗数据 (自由文本,表格,图像) 的非识别技术.
- 确定最先进的方法,评估策略和医学数据非识别方面的研究差距.
主要方法:
- 在五个数据库 (PubMed, Web of Science, DBLP, ACM, IEEE) 进行系统的文献搜索,从2015-2024年开始.
- 根据包含/排除标准和质量评估,过了734篇论文.
主要成果:
- 确定了83篇相关文章,其中大多数专注于自由文本去识别.
- 较少的研究涉及表格数据,更少的研究涉及嵌入医疗图像中的文本.
- 指出了英语语言数据集和内容非识别的普遍性.
结论:
- 不识别技术正在发展,转向语言模型.
- 现成的工具往往需要定制;代码混合和数据集稀缺性带来了挑战.
- 需要进一步的研究来解决多语言数据和数据集可供复制性的局限性.
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