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Standardized guidelines for the oral health management of patients with rare diseases exhibiting morphologic anomalies are currently lacking. This review considers Bardet-Biedl syndrome (BBS), a monogenic autosomal recessive nonmotile ciliopathy, as an archetypal condition. Dental anomalies are present in a majority of individuals affected by BBS due to abnormal embryonic orofacial and tooth development. Genetically encoded intrinsic oral structural anomalies and heterogeneous BBS clinical phenotypes and consequent oral comorbidities confound oral health management. Since the comorbid spectrum of BBS phenotypes spans diabetes, renal disease, obesity, sleep apnea, cardiovascular disease, and cognitive disorders, a broad spectrum of collateral oral disease may be encountered. The genetic impact of BBS on the anatomic development of oral components and oral pathology encountered in the context of various BBS phenotypes and their associated comorbidities are reviewed herein. Challenges encountered in managing patients with BBS are highlighted, emphasizing the spectrum of oral pathology associated with heterogeneous clinical phenotypic expression. Guidelines for provision of care across the spectrum of BBS clinical phenotypes are considered. Establishment of integrated medical-dental delivery models of oral care in the context of rare diseases is emphasized, including involvement of caregivers in the context of managing these patients with special needs.

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The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System’s data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (N validation ) of derived predictive models. Further, subsets of 30%–70%, 40%–60% and 50%–50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on N validation . We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%–50% case-control ratio outperformed other predictive models over N validation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians.

Background: International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations, where pneumonia is standardly subtyped by settings, exposures and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHR), frequently in non-structured formats including radiological interpretation or clinical notes that complicate electronic classification. Objective: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR. Methods: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for ‘rule of two’ pneumonia-related codes or one ICD code and radiologically-confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support sub-classification based on features including symptomatic patient point of entry into the healthcare system timing of pneumonia emergence and identification of clinical, laboratory or medication orders that informed definition of the pneumonia sub-classification algorithm. Results: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following NLP classification of pneumonia status as ‘negative’ or ‘unknown’. Subtyping of 83,387 episodes identified: community acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), healthcare-acquired (5%), ventilator-associated (0.4%) cases, and 9.4% were not classifiable by the algorithm. Conclusion: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.