A machine learning–based clinical decision support algorithm for reducing unnecessary coronary angiograms

Background: Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities. Objective: To reduce patient risk and cost to the healthcare system by improving …