Prediction and Machine Learning Models for Early Prediction of AKI

This chapter explores the intersection of machine learning (ML) and critical care, focusing on the prediction of acute kidney injury (AKI). AKI poses significant challenges in critical care settings due to delayed recognition and suboptimal management using traditional diagnostic criteria. ML algorithms offer a promising solution by analyzing diverse clinical datasets to predict AKI onset and facilitate early intervention. Seminal studies employing random forest and gradient boosting algorithms, as well as advanced deep learning techniques, demonstrate the potential of ML in AKI prediction. Integration of ML models into clinical decision support systems (CDSS) further enhances clinical decision-making and patient outcomes. Despite the promise of ML, challenges such as data interoperability, model validation, and ethical considerations must be addressed to facilitate seamless integration into clinical practice.