在分类和分组健康公平信息中的公平性
Ruinan Jin1, Xiaoxiao Li1, Lorraine J Block2
1The University of British Columbia, Vancouver and Okanagan, BC, Canada.
Studies in health technology and informatics
|July 25, 2024
概括
本研究研究了机器学习在预测抗菌治疗中的公平性. K-最近的邻居显示出最好的公平性,在语言分组中表现一致,分组更多的变量改善了公平性.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 医学伦理 医学伦理
背景情况:
- 预测抗微生物治疗在伤口护理中至关重要.
- 健康的社会决定因素,如语言和性别,可以影响医疗保健的获取和结果.
- 在机器学习模型中评估公平性对于防止健康差异至关重要.
研究的目的:
- 为了评估在机器学习分类器中公平性和性能之间的平衡,用于预测抗菌治疗.
- 分析分组语言代码对分类器公平性和性能的影响.
- 调查健康 (性别,语言) 的社会决定因素在模型公平性中的作用.
主要方法:
- 利用来自社区护理伤口护理电子健康记录的结构化数据.
- 采用了各种基于学习的统计学分类器.
- 评估了使用性别和语言作为预测因素的分类器公平性.
- 分析了不同语言代码分组对模型结果的影响.
主要成果:
- 在各种语言代码分组中,K-Nearest Neighbors表现出卓越的公平度指标.
- 尽管有不同的语言代码分组,但分类器的性能仍然保持一致.
- 将更多的变量分组起来,通常可以提高公平度量,而不会影响业绩.
结论:
- 机器学习模型可以针对医疗预测的公平性进行优化.
- 语言和性别数据是公平人工智能护理的重要考虑因素.
- 变量分组策略可以提高抗微生物治疗预测模型的公平性.
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