牙损的早期儿童预测因素:一种机器学习方法
L Toledo Reyes1, J K Knorst1, F R Ortiz2
1Department of Stomatology, Federal University of Santa Maria, Santa Maria, Brazil.
Journal of dental research
|May 29, 2023
概括
机器学习模型使用幼儿数据准确地预测儿童的牙损伤. 这些模型确定了主要的牙和永久牙的关键风险因素,有助于早期干预策略.
科学领域:
- 牙科 牙科是指牙科的专业.
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 牙损是一个重要的全球健康问题,特别是在儿童中.
- 预测皮的发展对于及时和有效的预防性干预至关重要.
- 现有的预测模型往往缺乏准确性或依赖于复杂的数据收集.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测初级和永久牙的.
- 在2年和10年的随访期间识别幼儿发育的早期预测因素.
- 与传统的物流回归相比,评估ML算法的性能.
主要方法:
- 在巴西南部进行了一项为期10年的前性队列研究,分析了1-5岁的639名儿童的数据.
- 牙损伤的评估是使用牙检测和评估系统 (ICDAS) 标准进行的.
- 机器学习算法 (决策树,随机森林,极端梯度提升) 和后勤回归被使用,模型性能使用接收器操作特征曲线 (AUC) 下的面积进行评估.
主要成果:
- ML模型在2年后实现了AUC>0.70,用于预测母牙的,基线严重程度是最强的预测因素.
- 10年后,基于XGBoost的模型 (使用SHAP) 实现了永久牙的AUC>0.70.
- 永久性牙的主要预测因素包括先前牙经验,不使用化牙膏,家长教育,糖消费,社交互动频率和家长对口腔健康的看法.
结论:
- 机器学习方法显示出在初级牙和永久牙中准确预测牙的巨大潜力.
- 幼儿时期容易收集的预测因素可以被ML模型有效地用于脏病预后.
- 这些发现支持制定针对性,数据驱动的儿童脏病预防策略.
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