检查不同糖尿病患者电子健康记录中的语言差异:自然语言处理分析
Isabel Bilotta1, Scott Tonidandel2, Winston R Liaw3
1Deutser, Houston, TX, United States.
JMIR medical informatics
|May 24, 2024
概括
这项研究使用自然语言处理来分析电子健康记录的笔记,发现可能表明临床医生对种族和少数民族有偏见的语言标记. 这种方法可以帮助识别和减少偏见,以改善健康公平.
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.
- 健康差距 研究 研究 研究 研究
背景情况:
- 少数民族种族和民族群体面临着严重的健康差异,部分原因是临床医生的偏见.
- 电子健康记录 (EHR) 笔记可能包含反映这种偏差的语言标记.
研究的目的:
- 通过使用自然语言处理 (NLP) 来调查基于患者的种族和种族的EHR笔记中的语言差异.
- 通过评估临床医生对与语言标记相关的偏见的看法来验证NLP发现.
主要方法:
- 一项横截面研究分析了超过12,000名糖尿病患者 (白人,黑人,西班牙裔/拉丁裔) 的电子病历笔记.
- 分析了情感分析和社会认知引擎 (SEANCE) 的组件和词数.
- 线性混合效应模型检查了语言标记和患者种族/种族之间的关系,控制年龄.
- 临床医生评估了语言变异表明偏差的程度.
主要成果:
- 与白人患者相比,黑人患者的电子健康记录有更多的负面形容词和恐惧/厌恶词.
- 对西班牙裔/拉丁裔患者的笔记显示,积极的形容词,信任动词和欢乐词比白人患者少.
- 临床医生认为负面形容词和恐惧/厌恶词语高度表明了偏见.
结论:
- 对EHR笔记的NLP分析可以识别可能与临床医生偏见相关的语言模式.
- 这种方法为医生和研究人员提供了检测和减轻偏见的工具.
- 减少医疗互动中的偏见对于解决健康差异至关重要.
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