在使用自然语言处理的产科临床笔记中识别污名化和积极/偏好的语言.
Jihye Kim Scroggins1, Ismael I Hulchafo1, Sarah Harkins1
1School of Nursing, Columbia University, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 21, 2024
概括
这项研究使用自然语言处理 (NLP) 来检测产科临床笔记中的污名化语言. 临床BERT模型有效地识别了偏见的语言,提高了护理质量.
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.
- 医疗保健偏见 医疗保健偏见
背景情况:
- 临床笔记中的污名化语言可以延续医疗保健差异.
- 识别和减轻这种语言对于公平的患者护理至关重要.
研究的目的:
- 开发和评估自然语言处理 (NLP) 模型,用于识别产科临床笔记中的污名化语言.
- 提高在电子健康记录中检测偏见语言的准确性和效率.
主要方法:
- 分析了2017年美国出生病例中的1771个产科临床笔记.
- 对标记语言类别的注释的注释.
- 使用基于语义相似性的搜索方法扩展数据集.
- 培训和验证传统的分类器和基于变压器的模型,包括临床BERT.
主要成果:
- 语义相似性方法成功地扩大了数据集,特别是对于低频类别.
- 所有NLP模型在数据集增强后都显示了性能改善.
- 临床BERT获得了0.78的最高平均F1得分,超过了其他模型.
结论:
- 临床BERT在分娩笔记中捕捉微妙的,取决于上下文的污名化语言方面表现出很高的有效性.
- 语义相似性方法提高了模型的性能,并减少了手动注释的努力.
- 这些发现支持NLP在实时监测中的潜力,以减少医疗保健偏见并促进公平的产周护理.
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