Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records.

ABSTRACT: Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regressi

SUBMITTER: Ghanzouri I 

PROVIDER: S-EPMC9349186 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature