使用机器学习对延迟的牙科护理进行分类:一项国家卫生访谈调查分析
Giang Vu1, Atish Chandra1, Sanket Salvi1
1Center for Decision Support Systems and Informatics, School of Global Health Management and Informatics, University of Central Florida, Orlando, Florida, USA.
International dental journal
|February 7, 2026
概括
延迟的牙科护理影响了14.5%的美国成年人,种族/少数民族和缺乏保险的人群的比例更高. 机器学习模型确定了延迟牙科护理的主要风险因素.
科学领域:
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
- 健康 公平 卫生 公平
背景情况:
- 延迟的牙科护理加剧了口腔健康问题,反映了更广泛的医疗保健准入差距.
- 了解延迟牙科护理的流行率和驱动因素对于解决口腔健康不平等至关重要.
研究的目的:
- 估计美国成年人在12个月内延迟牙科护理的患病率.
- 识别人口统计和社会经济群体之间延迟护理的差异.
- 评估机器学习 (ML) 在识别有风险的个体方面的实用性.
主要方法:
- 对2023年国家健康访谈调查 (NHIS) 成人样本文件 (N=54,927) 的横截面分析.
- 描述性统计数据按年龄,性别,教育,保险和种族/种族分层.
- 开发和评估ML分类器 (LightGBM),使用准确度,精度,回忆,F1分数,AUC和SHAP进行解释.
主要成果:
- 14.5%的美国成年人报告说,在过去的一年中,他们推迟了牙科护理.
- 在35-64岁的成年人,黑人/非洲裔美国人,多种族人士和教育程度较低的人群中观察到较高的延迟率.
- 缺乏牙科保险显著增加了延迟护理的可能性. 轻GBM实现了84.87%的准确性,教育,收入与贫困比率和保险状况作为关键预测因素.
结论:
- 延迟的牙科护理普遍存在,并且不成比例地影响特定的人口和社会经济群体.
- 可解释的ML模型可以帮助针对性干预的风险分层.
- 推扩大牙科覆盖范围和降低财务障碍的政策来缓解不平等.
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