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Browsing by Author "Siddiqui, Zasim"
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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 Differences in medication usage of dental patients by age, gender, race/ethnicity and insurance status(IOS, 2021) Siddiqui, Zasim; Wang, Yue; Patel, Jay; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistryBACKGROUND: Limited studies have investigated the medication profile of young adult dental patients despite the high prevalence of prescription opioid abuse in this population. OBJECTIVE: This study investigated the extent and differences in medication usage of dental patients older than 18 years by age, race/ethnicity, gender, insurance status and mechanism of action in an academic dental clinic setting. METHODS: Using an automated approach, medication names in the electronic dental record were retrieved and classified according to the National Drug Code directory. Descriptive statistics, multivariable ANOVA and Post hoc tests were performed to detect differences in the number of medications by patient demographics. RESULTS: Of the 11,220 adult patients, 53 percent reported taking at least one medication with significant differences in medication usage by demographics. Hydroxymethylglutaryl-coenzyme A reductase inhibitors (21–36%), and angiotensin-converting enzyme inhibitors (19–23%) ranked the top two medication classes among patients 55 years and older. Opioid agonists (7–14%), and Selective Serotonin Reuptake Inhibitors (SSRIs) (5–12%) ranked the top two medication classes among patients aged 18–54 years. CONCLUSIONS: The results underscore the importance of dental providers to review medical and medication histories of patients regardless of their age to avoid adverse events and to determine patient’s risk for opioid abuse.Item Evaluation of a Dental Diagnostic Terminology Subset(IOS, 2019) Taylor, Heather L.; Siddiqui, Zasim; Frazier, Kendall; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistryThe objective of this study was to determine how well a subset of SNODENT, specifically designed for general dentistry, meets the needs of dental practitioners. Participants were asked to locate their written diagnosis for tooth conditions among the SNODENT terminology uploaded into an electronic dental record. Investigators found that 65% of providers’ original written diagnoses were in “agreement” with their selected SNODENT dental diagnostic subset concept(s).Item Extraction and Evaluation of Medication Data from Electronic Dental Records(IOS Press, 2017) Wang, Yue; Siddiqui, Zasim; Krishnan, Anand; Patel, Jay; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistryWith an increase in the geriatric population, dental care professionals are presented with older patients who are managing their comorbidities using multiple medications. In this study, we developed a system to extract medication information from electronic dental records (EDRs) and provided patient distribution by the number of medications.Item Extraction and Evaluation of Medication Data from Electronic Dental Records(IOS Press, 2017) Wang, Yue; Siddiqui, Zasim; Krishnan, Anand; Patel, Jay; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of DentistryWith an increase in the geriatric population, dental care professionals are presented with older patients who are managing their comorbidities using multiple medications. In this study, we developed a system to extract medication information from electronic dental records (EDRs) and provided patient distribution by the number of medications.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 Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices(Thieme, 2020-03) Thyvalikakath, Thankam Paul; Duncan, William D.; Siddiqui, Zasim; LaPradd, Michelle; Eckert, George; Schleyer, Titus; Rindal, Donald Brad; Jurkovich, Mark; Shea, Tracy; Gilbert, Gregg H.; Pediatrics, School of MedicineObjectives: The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR). Methods: Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location. Results: Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset. Conclusion: This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice.Item Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity(Thieme, 2018) Patel, Jay; Siddiqui, Zasim; Krishnan, A.; Thyvalikakath, Thankam Paul; Cariology, Operative Dentistry and Dental Public Health, School of DentistryBackground Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. Objective To determine patients' detailed smoking status based on smoking intensity from the EDR. Methods First, the authors created a reference standard of 3,296 unique patients’ smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients’ smoking histories. Results Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients’ detailed smoking information. Conclusion EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR.