Do transformers generalise better than bespoke tools for anonymisation?

Q1 Medicine
Roman Klapaukh , Carol El-Hayek , Douglas IR Boyle
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引用次数: 0

Abstract

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
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.
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