Patel, JayMowery, DanielleKrishnan, AnandThyvalikakath, Thankam2018-11-262018-11-262018https://hdl.handle.net/1805/17825Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data.en-USCardiovascular DiseaseElectronic Medical RecordsElectronic Dental RecordsNatural Language ProcessingCardiovascular DiseasesElectronic Health RecordsNatural Language ProcessingArticle