Section of Public and Population Health, Division of Oral and Systemic Health Sciences, School of Dentistry, University of California, Los Angeles, Los Angeles, California, United States of America.
Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, United States of America.
PLoS One. 2024 Mar 22;19(3):e0299947. doi: 10.1371/journal.pone.0299947. eCollection 2024.
Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.
This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.
We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.
The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.
调查可以帮助对人群中的口腔疾病进行筛查，以提高对有需要的儿童疾病的早期发现和干预策略。本文旨在为 8-17 岁学龄儿童的活跃性龋齿和紧急治疗需求开发儿童报告和代理报告调查筛查工具的简短形式。
本横断面研究于 2015 年至 2019 年期间从洛杉矶县的 14 家牙科诊所和私人诊所招募了 497 对不同的儿童-家长对，这些儿童年龄在 8-17 岁之间。我们评估了 88 项儿童报告和 64 项代理报告的口腔健康问题的回答，以使用项目反应理论选择和校准简短形式。使用短表单和家庭人口统计学变量，使用七种经典机器学习算法来预测儿童的活跃性龋齿和紧急治疗需求。候选算法包括 CatBoost、Logistic Regression、K-Nearest Neighbors (KNN)、朴素贝叶斯、神经网络、随机森林和支持向量机。使用重复的 5 折嵌套交叉验证评估预测性能。
我们开发并校准了四个十项简短形式。朴素贝叶斯的交叉验证 ROC 曲线下面积的中位数最高，优于其他算法。使用儿童报告和代理报告的最佳测试敏感度和特异性的平均值分别为活跃性龋齿 0.84 和 0.30，紧急治疗需求为 0.81 和 0.31。纳入两种反应类型的模型比仅依赖儿童报告或代理报告的模型显示出略高的预测准确性。
项目反应理论和机器学习算法的结合为儿童的活跃性龋齿和紧急治疗需求提供了潜在有用的筛查工具。在处理大量人群的口腔健康评估时，调查筛查方法具有相对成本效益和方便性。未来的研究需要进一步利用基于人群特定亚组估计的项目特征定制和改进工具，以提高预测准确性。
