Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure

Abstract

This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. The actionability evaluation demonstrates a possible opportunity to intervene as part of a CS algorithm for escalation of care.