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Item Characterizing Restorative Dental Treatments of Sjögren's Syndrome Patients Using Electronic Dental Records Data(IOS Press, 2017) Siddiqui, Zasim; Wang, Yue; Makkad, Payal; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistryScant knowledge exists on the type of restorative treatments Sjögren's syndrome patients (SSP) receive in spite of their high dental disease burden due to hyposalivation. Increased adoption of electronic dental records (EDR) could help in leveraging information from these records to assess dental treatment outcomes in SSP. In this study, we evaluated the feasibility of using EDR to characterize the dental treatments SSP received and assess the longevity of implants in these patients. We identified 180 SSP in ten years of patients' data at the Indiana University School of Dentistry clinics. A total of 104 (57.77%) patients received restorative or endodontic treatments. Eleven patients received 23 implants with a survival rate of 87% at 40 months follow-up. We conclude that EDR data could be used for characterizing the treatments received by SSP and for assessing treatment outcomes.Item Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records(MDPI, 2023-03-08) Patel, Jay S.; Kumar, Krishna; Zai, Ahad; Shin, Daniel; Willis, Lisa; Thyvalikakath, Thankam P.Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Results: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients’ disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.Item Identifying Patients' Smoking Status from Electronic Dental Records Data(IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistrySmoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.Item Identifying Patients' Smoking Status from Electronic Dental Records Data(IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistrySmoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.Item Survival Analysis of Endodontically Treated Teeth in Patients with Diabetes and Hypertension within National Dental PBRN Practices(2022-06) Crosby, William Justin; Spolnik, Kenneth; Thyvalikakath, Thankam Paul; Ehrlich, Ygal; Warner, NedIntroduction: The prevalence of diabetes mellitus (DM) is rapidly increasing among the aging United States population. This poses a challenge to dental providers since DM and multiple oral conditions have been identified as comorbidities. Hypertension (HTN) is associated with more poorly controlled DM and has been identified as contributing to RCT tooth loss in prior studies. Links have also been established between DM and the survival rate of root canal treated teeth, however, previous research has focused on institutional settings despite the majority of RCT being performed in private dental practices. This study will use data from private dental practices to evaluate the survival rate of RCT teeth in patients with DM and HTN. Materials and Methods: This retrospective study evaluated the survival rate of endodontic treated teeth among patients with DM and HTN using National Dental PBRN Practice data. Electronic dental records from 42 private dental practices in the United States over a period of 15 years with a minimum 2-year follow-up comprising 11,532 root canal treated teeth were analyzed. Kaplan-Meier survival curves were used to demonstrate the effects of HTN and DM on RCT tooth survival and Cox proportional hazards survival analysis was used to evaluate the DM and HTN effects after accounting for age, gender, insurance, year of treatment, tooth type, and crown and filling placement as covariates. Results: Patients with HTN only had significantly lower risk of failure than patients with both HTN and DM (p=0.003). Patients with neither HTN nor DM had significantly lower risk of failure than patients with both HTN and DM (p=0.020). Patients with DM only did not have significantly different risk of failure than patients with both HTN and DM (p=0.223). Patients with DM only did not have significantly different risk of failure than patients with HTN only (p=0.361). Patients with neither HTN nor DM did not have significantly different risk of failure than patients with HTN only (p=0.121) or patients with DM only (p=0.800). Conclusions: Patients with both DM and HTN have an increased chance of root canal treated tooth failure while patients with only DM or only HTN do not. Evaluation of severity of DM may be more important in determining RCT failure and studies utilizing laboratory values should be considered for future research.