ArticleMethods of information in medicine2022
Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records.
Article in Methods of information in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Evaluating the Performance and Implementation of the 2018 Classification of Periodontal Diseases: A Systematic Review and Survey.Journal of clinical periodontology · 2025Pooled it
- Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.JDR clinical and translational research · 2026Article
- The Application and Performance of Artificial Intelligence (AI) Models in the Diagnosis, Classification, and Prediction of Periodontal Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Cross-institutional dental electronic health record entity extraction via generative artificial intelligence and synthetic notes.JAMIA open · 2025Article
- Development and Comparative Evaluation of a Reinstructed GPT-4o Model Specialized in Periodontology.Journal of clinical periodontology · 2025Article
- Development of a Machine Learning Tool for Home-Based Assessment of Periodontitis.medRxiv : the preprint server for health sciences · 2025Article
- Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation.Heliyon · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data.Frontiers in artificial intelligence · 2022Article
- Enhancing an AI-Empowered Periodontal CDSS and Comparing with Traditional Perio-risk Assessment Tools.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOur objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms.
methodsWe conducted a retrospective study using EDR data of patients (
resultsThe completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% (
conclusionsWe successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.