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预测口腔健康相关生活质量从青春晚期到早期成年期的基于群体的轨迹使用K-Means集群算法
Chukwuebuka Ogwo1, Grant Brown2, John Warren3
1Department of Oral Health Policy and Epidemiology, Harvard University School of Dental Medicine, Boston, Massachusetts, USA.
Journal of public health dentistry
|September 16, 2025
概括
大多数年轻人从青春期到成年早期都保持了良好的口腔健康相关生活质量 (OHRQoL). 更高的社会经济地位与有利的OHRQoL轨迹有关,这表明需要针对风险群体进行有针对性的干预.
科学领域:
- 口腔卫生流行病学 口腔卫生流行病学
- 纵向队列研究是指纵向队列研究.
- 机器学习在健康研究中的应用.
背景情况:
- 与口腔健康相关的生活质量 (OHRQoL) 对整体健康至关重要.
- 了解从青春期到早期成年期的OHRQoL变化对公共卫生很重要.
- 纵向数据可以揭示随时间变化的模式.
研究的目的:
- 分析年龄在17至23岁的人的OHRQoL轨迹模式.
- 利用机器学习来识别不同的OHRQoL变化组.
- 检查与OHRQoL轨迹相关的社会人口统计因素.
主要方法:
- 分析了来自爱荷华州化物研究 (IFS) 的纵向数据.
- 与口腔健康相关的生活质量 (OHRQoL) 在17岁,19岁和23岁时使用验证的问卷 (CPQ11-14,GOHR,VisQoL) 进行评估.
- 纵向数据的K-Means (KmL) 算法确定了轨迹组,后勤回归检查了与社会人口统计学相关的关联.
主要成果:
- 确定了两个不同的OHRQoL轨迹组:持续改善与持续恶化.
- 比例因仪器而异,但方向趋势是一致的.
- 更高的社会经济地位与有利的OHRQoL轨迹显著相关 (p <0.05).
结论:
- 大多数参与者表现出有利的OHRQoL轨迹.
- 较高社会经济背景的个人中,有利的轨迹更为常见.
- 纵向的,多项措施的方法有效地识别了面临较差OHRQoL风险的子组.
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