预测机器学习算法的公平性放弃了成人预防性牙科护理
Helena Silveira Schuch1, Mariane Furtado1, Gabriel Ferreira Dos Santos Silva2
1Harvard School of Dental Medicine, Harvard University, Boston, Massachusetts.
JAMA network open
|November 3, 2023
概括
机器学习模型可以预测可能会跳过预防性牙科护理的成年人. 然而,这些模型可能会显示对某些社会人口统计学群体的偏见,突出了公平评估的必要性.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 牙科公共卫生 牙科公共卫生
背景情况:
- 预防性牙护理对于整体健康和预防晚期牙疾病至关重要.
- 识别面临风险的人群是针对性公共卫生干预的关键.
研究的目的:
- 开发和评估机器学习模型,用于预测成年人的预防性牙科护理.
- 评估这些模型在社会人口统计学子组中的算法公平性.
主要方法:
- 来自美国医疗支出小组调查 (MEPS) (2016-2019) 的纵向数据的二次分析.
- 利用以树为基础的集体机器学习模型与50个预测器.
- 使用接收器操作特征曲线 (AUC) 下面的面积来评估模型性能.
主要成果:
- 模型准确地预测了失去了预防性牙科护理 (总体AUC=0.84).
- 在社会人口统计学子组中,表现各不相同,代表性不足的群体的准确性较低.
- 以前的牙科访问模式,医疗保健利用率和牙科福利是关键预测因素.
结论:
- 机器学习模型在识别有缺失预防性牙科护理风险的成年人方面表现有前途.
- 观察到对代表性不足的群体的算法偏见,强调了公平性评估的必要性.
- 确保模式的公平性至关重要,以避免加剧现有的健康差异.
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