使用自然语言处理来识别劳动和分娩中的污名化语言 临床笔记
Veronica Barcelona1, Danielle Scharp2, Hans Moen3
1School of Nursing, Columbia University, 560 West 168th St, Mail Code 6, New York, NY, 10032, USA. vb2534@cumc.columbia.edu.
Maternal and child health journal
|December 26, 2023
概括
研究人员开发了自动化自然语言处理 (NLP) 方法来检测临床笔记中的污名化语言,实现了对边缘化和权力/特权语言检测的高准确性.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 医学社会学 医学社会学
背景情况:
- 种族和社会偏见导致怀孕和分娩结果的差异.
- 分析电子健康记录 (EHR) 中的污名化语言是识别偏见的新兴方法.
- 需要自动化方法来有效地检测临床笔记中的偏见语言.
研究的目的:
- 开发和评估自动化自然语言处理 (NLP) 方法,用于识别劳动和出生记录中的污名化语言.
- 准确检测两种类型的污名化语言:边缘化语言和权力/特权语言.
- 评估机器学习算法在分类这些语言类别中的性能.
主要方法:
- 来自两个医院的劳动和分娩记录的分析,用于超过20周怀孕的患者.
- 应用文本预处理技术,包括TF-IDF值.
- 机器学习分类器的测试:决策树,随机森林和支持矢量机器.
- 使用InfoGain进行特征重要性评估,以确定关键的语言标记.
主要成果:
- 决策树在边缘化语言方面获得了最佳分类,F分数为0.73.
- 支持矢量机器在权力/特权语言方面表现最佳,F分数为0.91.1.
- 该研究证实了精选的机器学习方法在分类污名化语言方面的有效性.
结论:
- 确定了性能良好的机器学习方法,用于在临床笔记中自动检测污名化语言.
- 这项研究是第一个使用NLP性能指标来评估机器学习以辨别污名化语言的研究.
- 未来的研究应该专注于改进NLP方法和探索先进的深度学习算法.
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