부산대학교 도서관

LGBT Patients bear a disproportional burden of health disparities. Sexual orientation and gender identity is of clinical relevance to healthcare providers and data scientists. However, little work has been done to identify LGBT patients, especially in data derived from electronic health record notes. We developed a prototype application that leverages machine learning and rule-based pattern matching methods to identify LGBT patients in a large data source, Veterans Health Administration electronic health record notes. This application achieved 88.2% sensitivity, 91.5% specificity, and 85.9% positive predictive value in a binary classification task for three random document test sets. This work has implications in both improved healthcare and data research.