验证Transformer在真实医疗场景电子健康记录文本脱敏中的有效性
Validating transformers for redaction of text from electronic health records in real-world healthcare
AI总结：
针对医疗记录脱敏精度低、适配性差的问题，本研究提出基于Transformer的AnonCAT模型，在英国3家医院3116份记录上取得高召回率，为真实医疗场景去标识化算法的部署与优化提供了可行蓝图。
摘要：
保护医疗记录中的患者隐私是首要任务，脱敏是遮蔽文本中直接可识别信息的常用方法。基于规则的方法已被广泛使用，但其精度通常较低，会导致文本过度脱敏，且往往不足以适配非标准化或非常规结构的个人健康信息。深度学习技术已成为一种有前景的解决方案，但由于不同科室、医院和国家的患者记录结构和语言存在差异，在真实环境中部署这些技术面临挑战。\n在本研究中，我们提出了AnonCAT——一种基于Transformer的模型，以及一套去标识化模型在真实医疗场景中部署的方案蓝图。AnonCAT的训练过程使用了来自英国3家采用不同电子健康记录系统的医院的真实文档人工标注脱敏数据，涉及3116份文档。该模型在全部3家医院中均取得了高性能，召回率分别为0.99、0.99和0.96。\n我们的研究结果证明了深度学习技术在提升全球医疗数据脱敏效率与准确性方面的潜力，同时强调了构建工作流的重要性：这类工作流不仅要使用这些模型，还需能够持续微调并审计算法性能，以确保其在真实场景中的持续有效性。该方法通过微调和本地化，为去标识化算法的真实应用提供了蓝图，相关代码与教程已在GitHub发布（https://github.com/CogStack/MedCAT）。
英文摘要：
Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text. Rule-based methods have been widely used, but their precision is often low causing over-redaction of text and frequently not being adaptable enough for non-standardised or unconventional structures of personal health information. Deep learning techniques have emerged as a promising solution, but implementing them in real-world environments poses challenges due to the differences in patient record structure and language across different departments, hospitals, and countries. In this study, we present AnonCAT, a transformer-based model and a blueprint on how deidentification models can be deployed in real-world healthcare. AnonCAT was trained through a process involving manually annotated redactions of real-world documents from three UK hospitals with different electronic health record systems and 3116 documents. The model achieved high performance in all three hospitals with a Recall of 0.99, 0.99 and 0.96. Our findings demonstrate the potential of deep learning techniques for improving the efficiency and accuracy of redaction in global healthcare data and highlight the importance of building workflows which not just use these models but are also able to continually fine-tune and audit the performance of these algorithms to ensure continuing effectiveness in real-world settings. This approach provides a blueprint for the real-world use of de-identifying algorithms through fine-tuning and localisation, the code together with tutorials is available on GitHub (https://github.com/CogStack/MedCAT).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
