Background Risk stratification and outcome prediction are crucial for intensive care resource
planning. In addressing the large data sets of intensive care unit (ICU) patients, we …

Background This study aimed to create a method for promptly predicting acute kidney injury
(AKI) in intensive care patients by applying interpretable, explainable artificial intelligence …

Abstract Acute Kidney Injury (AKI) is a sudden episode of kidney failure that is frequently
seen in critically ill patients. AKI has been linked to chronic kidney disease (CKD) and …

Purpose To predict acute kidney injury (AKI) in a large intensive care unit (ICU) database.
Materials and methods A total of 30,020 ICU admissions with 17,222 AKI episodes were …

Background Acute kidney injury is common in the surgical intensive care unit (ICU). It is
associated with poor patient outcomes and high healthcare resource usage. This study's …

Abstract The development of Acute Kidney Injury (AKI) during admission to the Intensive
Care Unit (ICU) is associated with increased morbidity and mortality. The objective of this …

Background To derive and validate a machine learning (ML) prediction model of acute
kidney injury (AKI) that could be used for AKI surveillance and management to improve …

Abstract Background There are many Machine Learning (ML) models which predict acute
kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to …

Acute kidney injury (AKI) is a common clinical condition with high mortality and resource
consumption. Early identification of high-risk patients to achieve an appropriate allocation of …

Purpose The goal of this study is to construct a mortality prediction model using the XGBoot
(eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the …