评估BERT对患者安全的可转移性:对多种类型的事件报告进行分类
1Centre for Health Informatics, Australia Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
BMJ health & care informatics
|August 19, 2025
概括
变压器的双向编码器表示 (BERT) 有效地对患者安全事件报告进行分类,优于CNN. 这种自然语言处理模型改善了罕见事件的识别,并对新数据进行了很好的概括.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的机器学习
- 患者安全信息学
背景情况:
- 患者安全事件的分类对于改善医疗保健质量至关重要.
- 传统方法在事件报告中常见的有限,不平衡和多类数据集中扎.
- 自然语言处理 (NLP) 模型为非结构化事件报告的自动分析提供了潜力.
研究的目的:
- 评估来自变压器的双向编码器表示 (BERT) 的可转移性和性能,用于对患者安全事件报告进行分类.
- 评估BERT在处理患者安全数据数据稀缺性和阶级不平衡方面的有效性.
- 在此分类任务中,比较BERT与卷积神经网络 (CNN) 的性能.
主要方法:
- 伯特被微调并用于词嵌入提取,以分类事件类型和严重程度.
- 训练数据包括来自澳大利亚全州系统的2860种事件类型和1160种严重程度.
- 在平衡,不平衡和独立数据集上评估性能,与使用BERT和本地嵌入的CNN进行比较.
主要成果:
- 精心调整的BERT显著超过了小型CNN,在事件类型分类中获得F分数超过89%.
- 伯特 (BERT) 显示出优于现实世界不平衡数据的概括性,提高了高达30.3%的罕见事件类型识别.
- 中等和低严重程度的F分数的改善范围从3.6%到19.7%不等.
结论:
- 精细调整的BERT提高了识别罕见类别和概括到未见的患者安全数据的性能.
- 伯特的处理数据稀缺和不平衡的能力使其成为患者安全分类的宝贵工具.
- 这种NLP方法为患者安全数据分析中常见的挑战提供了有希望的解决方案.
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