开发和验证一种自然语言处理算法,以在临床数据仓库的背景下假名化文档
Xavier Tannier1, Perceval Wajsbürt2, Alice Calliger2
1Sorbonne Université, Inserm, Université Sorbonne Paris Nord, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé (LIMICS), Paris, France.
Methods of information in medicine
|March 5, 2024
概括
这项研究引入了一种混合系统来消除临床报告的识别,达到0.99 F1分数. 它共享指南和代码,以促进研究,同时保护患者的隐私.
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:
- 消除临床报告的身份对于研究访问至关重要.
- 在数据共享期间必须保持患者隐私.
- 分享识别工具和资源带来了一些挑战.
研究的目的:
- 实施对临床文件进行系统的假名化.
- 为了应对消除身份工具共享的挑战.
- 为了使研究人员能够访问临床数据,同时确保隐私.
主要方法:
- 标注了12种实体类型的临床文件集.
- 开发了一种混合型的身份删除系统.
- 将深度学习模型与手册规则合并.
主要成果:
- 实现了整体F1得分为0.99的非识别.
- 分析了影响去识别工作的因素 (数据集大小,文档类型,模型,规则).
结论:
- 混合系统有效地消除了临床报告的身份.
- 分享指导方针和代码以促进进一步的研究.
- 系统的假名化提高了临床数据的可访问性,用于研究.
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