加强临床文本的隐私:一种新的方法来消除巴西临床叙述的身份
Elisa T R Schneider1, Fernando Schneider1, Yohan B Gumiel1
1Instituto do Coração - InCor/HC FMUSP, São Paulo (SP), Brazil.
Studies in health technology and informatics
|August 8, 2025
概括
本研究提出了一种使用先进的人工智能模型和规则去识别葡萄牙临床文本的新方法. 这种方法提高了数据隐私,同时保持了对代表性不足的语言的文本实用性.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 数据 隐私 数据 隐私 数据
背景情况:
- 不识别临床叙述对于隐私和监管合规性至关重要.
- 现有的方法可能无法充分解决像葡萄牙语这样代表性不足的语言的细微差别.
- 基于变压器的模型为复杂的文本分析提供了潜力.
研究的目的:
- 开发和评估一种新的混合方法,以消除葡萄牙临床叙述的识别.
- 为了提高临床文本匿名化的准确性和效率.
- 确保数据实用性,同时遵守隐私法规.
主要方法:
- 在葡萄牙临床文本 (心脏病学,肺病学) 上微调一个BioBERTpt变压器模型.
- 将微调的BioBERTpt模型与基于规则的技术 (正则表达式) 集成.
- 使用精度,回忆和F1分数来评估混合动力车型的性能.
主要成果:
- 混合BioBERTpt和基于规则的模型实现了高性能:精度 (0.92),回忆 (0.93),和F1得分 (0.93).
- 拟议的方法显著超过了基线去识别模型的性能.
- 这种方法有效地平衡了数据匿名化和数据实用性.
结论:
- 这种新的混合方法提供了一个强大的解决方案来消除葡萄牙临床叙述的识别.
- 这种方法证明了临床文本匿名化在资源有限的语言中的巨大潜力.
- 该研究有助于在医疗保健中推进保护隐私的人工智能,用于各种语言背景.
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