在一个单一中心的多专业评估评估中对医疗记录去识别的基于变压器的模型进行基准测试
Rachel Kuo1,2, Andrew A S Soltan2,3,4,5, Ciaran O'Hanlon1,2
1Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
iScience
|December 24, 2025
概括
电子健康记录的自动化非识别对研究至关重要. 评估变压器和大型语言模型表明,微软Azure服务实现了最高的性能,接近临床医生的准确性.
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:
- 患者保密对于电子健康记录 (EHR) 研究至关重要.
- 手动编辑敏感信息是耗时的,不可扩展的.
- 自动化去识别方法为数据共享提供了一个有前途的替代方案.
研究的目的:
- 对临床记录的各种自动化非识别模型的性能进行评估.
- 将特定任务的变压器模型与大型语言模型 (LLM) 进行比较.
- 评估不同非识别方法的准确性和适应性.
主要方法:
- 在3650个英国临床记录中评估了四个基于变压器的模型和五个LLM.
- 临床医生利用双重注释进行精确的性能比较.
- 在一般和专业数据集中评估模型.
主要成果:
- 微软Azure无标识服务获得了最高的F1分,与临床医生的表现相当.
- 微调的AnonCAT和GPT-4-0125 (几次拍摄) 也表现出了强的结果.
- 较小的LLM表现出过度删除和幻觉的问题;特定任务模型显示出更好的数据集稳定性.
结论:
- 自动化去识别系统可以有效地支持大规模的临床记录共享.
- 模型选择,适应策略和隐私保护措施对于成功实施至关重要.
- 这些系统提高了数据的实用性,同时确保了强大的患者隐私.
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