变形金刚比定制的匿名化工具更能泛化吗？

Q1 Medicine

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI:10.1016/j.imu.2024.101607

Roman Klapaukh , Carol El-Hayek , Douglas IR Boyle

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引用次数: 0

摘要

临床记录中的自由文本字段包含可能不会显示在结构化健康记录中的信息。自动匿名化工具可以降低大规模使用这些数据的门槛。然而，现有的匿名工具在其国家和原产领域之外使用时并不总是表现得像预期的那样好。我们在300个澳大利亚全科医生的笔记上运行了三个美国三级医疗目标变压器模型，并表明它们比专用工具更好地概括。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Do transformers generalise better than bespoke tools for anonymisation?

Free-text fields in clinical records contain information that may not show up in the structured health record. Automated anonymisation tools can lower the bar to using this data at scale. However, existing anonymisation tools do not always perform as well as expected when used outside of their country and domain of origin. We ran three US tertiary care targeting transformer models on 300 Australian general practice notes, and showed that they generalise better than purpose built tools.

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来源期刊

Informatics in Medicine Unlocked Medicine-Health Informatics

CiteScore

9.50

自引率

0.00%

发文量

282

审稿时长

39 days

期刊介绍： Informatics in Medicine Unlocked (IMU) is an international gold open access journal covering a broad spectrum of topics within medical informatics, including (but not limited to) papers focusing on imaging, pathology, teledermatology, public health, ophthalmological, nursing and translational medicine informatics. The full papers that are published in the journal are accessible to all who visit the website.