Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.

INTRODUCTION

Periodontal disease (PD) is closely linked to systemic health, with established associations with chronic conditions (eg, diabetes, cardiovascular disease). However, most predictive models rely solely on dental data, limiting the consideration of systemic factors such as medical conditions.

OBJECTIVES

This study aimed to enhance PD risk prediction by using linked electronic dental records (EDRs) with electronic health records (EHRs) and machine learning (ML).

METHODS

We used EDR data from 20,946 adult patients at Temple University School of Dentistry's (2022-2023) axiUm®, linked with medical data (physician documented) from the Pennsylvania Health Share Exchange. The dataset includes demographics, dental diagnoses, medical history, medications, procedures, and social determinants of health. The target variable was PD. Because EHR data are not research ready, extensive preprocessing was required (eg, 1 patient may have 400+ medical codes, which ML/statistical models cannot process directly). To prepare for artificial intelligence/ML, we developed 5 automated feature reduction approaches to retain rich information while reducing variables. After preprocessing, 106 features were retained as independent variables. ML models (Gaussian Naive Bayes, Random Forest, LightGBM, XGBoost) were trained using cross-validation across 5 experimental strategies, including (1) features selected via chi-square test, (2) raw data (without extensive processing), (3) aggregated data, (4) systemic disease complexity system, and (5) EHR-only data. Model performance was assessed using sensitivity, specificity, and area under the curve (AUC).

RESULTS

The chi-square-selected features yielded the best performance: 85% specificity, 67% sensitivity, and 84% AUC. Although adding medical conditions did not significantly improve overall performance, key conditions (eg, cardiovascular diseases, endocrine/metabolic disorders, renal diseases, respiratory conditions, hematologic disorders, etc) contributed meaningfully to PD risk prediction. EDR factors (oral hygiene, periodontal treatment, brushing, flossing, smoking, and American Society of Anesthesiologists classification) dominated prediction.

CONCLUSION

Although dental factors remained dominant predictors, strong systemic-oral health associations were observed. Future studies should validate these findings by integrating medical and dental records.Knowledge Transfer Statement:The results of this study can guide clinicians and policymakers in identifying patients at increased risk of periodontitis by integrating medical and dental records. This approach supports earlier interventions and highlights the importance of systemic health in oral disease management. It also demonstrates the potential of artificial intelligence-based prediction models to improve personalized care and promote interdisciplinary collaboration for better overall health outcomes.