POS-178 ENSEMBLE TREE-BASED MACHINE LEARNING ALGORITHM TO PREDICT ACUTE KIDNEY REPLACEMENT THERAPY IN CRITICALLY ILL PATIENT

Abstract

Critically ill patients were associated with severe acute kidney injury (AKI) and acute kidney replacement therapy (AKRT). The better prediction of AKRT could result in better triage, rapid management, and the best outcomes. Hence, some studies try to predict AKRT by clinical risk score or biomarkers. In contrast, we leverage information in routine clinical practice corporate with machine learning algorithms to predict AKRT. The objective is to create machine learning models to predict AKRT in the critically ill.