Patel J, Badi M, Katiyar R, Ogwo C, Wiener R C, Tiwari T, Sambamoorthi U, Folks T
Center for Dental Informatics and Artificial Intelligence, Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
J Dent Res. 2025 Sep;104(10):1085-1094. doi: 10.1177/00220345251328968. Epub 2025 May 4.
The impact of social determinants of health (SDoH) on periodontal disease (PD) is critical to study, as a deeper understanding of SDoH offers significant potential to inform policy and help clinicians provide holistic patient care. The use of machine learning (ML) to analyze the association of SDoH with PD provides significant advantages over traditional statistical methods. While statistical approaches are effective for identifying trends, they often struggle with the complexity and unstructured nature of data from dental electronic health records (DEHRs). The objective of this study was to determine the association between PD and SDoH using big data through linked DEHR and census data using ML. We used the records of 89,937 unique patients (754,414 longitudinal records) from the Temple University School of Dentistry who received at least 1 treatment between 2007 and 2023. Patient PD outcomes were categorized based on progression, improvement, or no change, using longitudinal data spanning up to 16 y. We applied ML models, including logistic regression, Gaussian naive Bayes, random forest, and XGBoost, to identify SDoH predictors and their associations with PD. XGBoost demonstrated the best performance with 94% accuracy and high precision, recall, and F1 scores. SHapley Additive exPlanations (SHAP) values were used to provide explainable ML analysis. The leading predictors for PD progression were higher social vulnerability index, poverty, population density, fewer dental offices, more fast-food restaurants, longer travel times, higher stress levels, tobacco use, and multiple comorbidities. Our findings underscore the critical role of SDoH in PD progression and oral health inequity, advocating for the integration of these factors in PD risk assessment and management. This study also demonstrates the potential of big data analytics and ML in providing valuable insights for clinicians and researchers to study oral health disparities and promote equitable health outcomes.
健康的社会决定因素（SDoH）对牙周疾病（PD）的影响是研究的关键，因为更深入地了解SDoH为政策制定提供了巨大潜力，并有助于临床医生提供全面的患者护理。与传统统计方法相比，使用机器学习（ML）分析SDoH与PD的关联具有显著优势。虽然统计方法在识别趋势方面很有效，但它们常常难以应对牙科电子健康记录（DEHR）数据的复杂性和非结构化性质。本研究的目的是通过使用ML将DEHR与人口普查数据相链接，利用大数据来确定PD与SDoH之间的关联。我们使用了天普大学牙科学院89,937名独特患者（754,414份纵向记录）的记录，这些患者在2007年至2023年期间至少接受过1次治疗。使用长达16年的纵向数据，根据进展、改善或无变化对患者的PD结果进行分类。我们应用了ML模型，包括逻辑回归、高斯朴素贝叶斯、随机森林和XGBoost，以识别SDoH预测因素及其与PD的关联。XGBoost表现最佳，准确率达94%，且具有高精度、召回率和F1分数。使用SHapley值相加解释（SHAP）值来提供可解释的ML分析。PD进展的主要预测因素包括较高的社会脆弱性指数、贫困、人口密度、较少的牙科诊所、较多的快餐店、较长的出行时间、较高的压力水平、烟草使用和多种合并症。我们的研究结果强调了SDoH在PD进展和口腔健康不平等中的关键作用，主张将这些因素纳入PD风险评估和管理中。本研究还展示了大数据分析和ML在为临床医生和研究人员研究口腔健康差异及促进公平的健康结果提供有价值见解方面的潜力。
