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dc.creatorPatel, Jay
dc.creatorSu, Chang
dc.creatorAlbandar, Jasim M.
dc.creatorRao, Rishi
dc.creatorIyer, Vishnu
dc.creatorShi, Evan
dc.creatorWu, Huanmei
dc.identifier.citationPatel JS, Su C, Tellez M, Albandar JM, Rao R, Iyer V, Shi E and Wu H (2022) Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data. Front. Artif. Intell. 5:979525. doi: 10.3389/frai.2022.979525
dc.description.abstractDespite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.
dc.format.extent16 pages
dc.relation.ispartofFaculty/Researcher Works
dc.relation.haspartFrontiers in Artificial Intelligence, Vol. 5
dc.relation.isreferencedbyFrontiers Media
dc.rightsAttribution CC BY
dc.subjectDental informatics
dc.subjectPrediction model
dc.subjectElectronic dental record
dc.subjectElectronic health record
dc.subjectMachine learning
dc.subjectPeriodontal disease
dc.subjectSocial determinants of health
dc.titleDeveloping and testing a prediction model for periodontal disease using machine learning and big electronic dental record data
dc.type.genreJournal article
dc.description.departmentHealth Informatics
dc.description.departmentHealth Services Administration and Policy
dc.description.departmentOral Health Sciences
dc.description.departmentPeriodontology and Oral Implantology
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact
dc.description.schoolcollegeTemple University. College of Public Health
dc.description.schoolcollegeKornberg School of Dentistry
dc.temple.creatorPatel, Jay S.
dc.temple.creatorSu, Chang
dc.temple.creatorTellez, Marisol
dc.temple.creatorAlbandar, Jasim M.
dc.temple.creatorRao, Rishi
dc.temple.creatorIyer, Vishnu
dc.temple.creatorShi, Evan
dc.temple.creatorWu, Huanmei

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