不结构化的电子健康记录的OpenDeID管道 基于规则和变压器的文字笔记:去识别算法开发和验证研究
Jiaxing Liu1, Shalini Gupta2, Aipeng Chen3
1School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.
Journal of medical Internet research
|December 6, 2023
概括
本研究介绍了OpenDeID,这是一个混合系统,结合了规则和变压器,以消除电子健康记录中的敏感健康信息的识别. 该系统实现了高精度,证明了其对研究数据隐私的有效性.
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:
- 非结构化的电子健康记录 (EHR) 是临床和生物医学研究的丰富数据来源.
- 患者隐私需要在研究使用之前从电子健康记录中删除敏感健康信息 (SHI).
- 虽然存在基于规则和机器学习的方法来消除身份,但很少有研究将它们与变压器模型相结合.
研究的目的:
- 使用规则和变压器,为澳大利亚的EHR文本注释开发一条混合去识别管道.
- 调查预训练词嵌入和基于变压器的语言模型对非识别准确性的影响.
主要方法:
- 开发了OpenDeID管道,一种混合方法,集成关联规则,监督深度学习和预训练的语言模型.
- 利用了澳大利亚多中心EHR体的OpenDeID体,包括2,100个病理学报告和38,414个SHI实体.
- 微调了排放总结BioBERT模型,并纳入了加工前/加工后规则.
主要成果:
- 在OpenDeID管道中使用微调的BioBERT模型实现了0.9659的最佳F1得分.
- 开放DeID管道已经在一个大型高等教学医院成功部署.
- 实时处理了超过8000个非结构化的EHR文本笔记.
结论:
- 开放DeID管道提供了一种有效的混合方法,用于在非结构化的EHR文本注释中消除敏感健康信息的识别.
- 管道的性能已经在一个大型的多中心集体上得到验证.
- 未来的工作包括外部验证,以进一步评估管道的有效性.
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