Browsing by Subject "Dental Informatics"

Now showing 1 - 4 of 4
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    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.
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    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 Dentistry
    With 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.
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    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 Dentistry
    With 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.
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    Utilizing Electronic Dental Record Data to Track Periodontal Disease Change
    (2020-07) Patel, Jay Sureshbhai; Jones, Josette; Thyvalikakath, Thankam Paul; Palkal, Mathew; Kowolik, Michael; Nagarajan, Radha
    Periodontal disease (PD) affects 42% of US population resulting in compromised quality of life, the potential for tooth loss and influence on overall health. Despite significant understanding of PD etiology, limited longitudinal studies have investigated PD change in response to various treatments. A major barrier is the difficulty of conducting randomized controlled trials with adequate numbers of patients over a longer time. Electronic dental record (EDR) data offer the opportunity to study outcomes following various periodontal treatments. However, using EDR data for research has challenges including quality and missing data. In this dissertation, I studied a cohort of patients with PD from EDR to monitor their disease status over time. I studied retrospectively 28,908 patients who received comprehensive oral evaluation at the Indiana University School of Dentistry between January 1st-2009 and December 31st-2014. Using natural language processing and automated approaches, we 1) determined PD diagnoses from periodontal charting based on case definitions for surveillance studies, 2) extracted clinician-recorded diagnoses from clinical notes, 3) determined the number of patients with disease improvement or progression over time from EDR data. We found 100% completeness for age, sex; 72% for race; 80% for periodontal charting findings; and 47% for clinician-recorded diagnoses. The number of visits ranged from 1-14 with an average of two visits. From diagnoses obtained from findings, 37% of patients had gingivitis, 55% had moderate periodontitis, and 28% had severe periodontitis. In clinician-recorded diagnoses, 50% patients had gingivitis, 18% had mild, 14% had moderate, and 4% had severe periodontitis. The concordance between periodontal charting-generated and clinician-recorded diagnoses was 47%. The results indicate that case definitions for PD are underestimating gingivitis and overestimating the prevalence of periodontitis. Expert review of findings identified clinicians relying on visual assessment and radiographic findings in addition to the case definition criteria to document PD diagnosis.