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通过基于自然语言处理的系统 (ENGAGE) 在家庭医疗保健中识别和减少污名化的语言:混合方法研究协议

Zhihong Zhang^1,2, Pallavi Gupta², Stephanie Potts-Thompson^2,3

¹Columbia University Data Science Institute, New York, NY, United States.

JMIR research protocols

|September 25, 2025

概括

本研究使用自然语言处理 (NLP) 来识别和减少家庭医疗保健 (HHC) 临床笔记中的污名化语言. 该ENGAGE系统旨在通过检测和取代有害术语来改善患者护理.

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.
临床交流临床沟通

背景情况:

临床笔记中的污名化语言会对患者护理质量产生负面影响.
自然语言处理 (NLP) 为在电子健康记录中识别这种语言提供了一个解决方案.

研究的目的:

开发和评估一个NLP驱动的系统 (ENGAGE) 来自动识别和取代临床笔记中的污名化语言.
改进家庭医疗保健 (HHC) 设置中对污名化语言的理解.

主要方法:

一种混合方法方法,使用来自HHC组织的电子健康记录数据 (2019-2021年).
目标1:通过采访和笔记分析,完善污名化语言的本体学.
目标2:确定最佳的NLP准确性方法.
目标3:根据患者的种族/种族来分析患病率.
目标4:使用NLP方法和用户界面开发ENGAGE系统.

主要成果:

资金来自国家少数民族健康和健康差异研究所.
招聘开始于2024年5月;截至2025年3月,对9名参与者进行了面试.
所有研究目标预计将于2027年4月完成.

结论:

该研究将使用广泛的数据检查HC设置中的污名化语言.
旨在开发有效减少HHC护士之间耻辱化的语言的系统.
通过技术干预,有助于提高患者护理的质量.

关键词:

在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.家庭医疗保健家庭医疗保健自然语言处理自然语言处理.种族偏见是一种种族偏见.污名化的语言.

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