Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records

Abstract

Leveraging readily available clinical data from EHRs, we developed a machine-learning model for aGVHD prediction in patients undergoing HCT. Continuous monitoring of vital signs, such as temperature, could potentially help clinicians more accurately identify patients at high risk for aGVHD.