Background: The prediction model of hemodialysis prescription is established based on
machine learning to achieve precise hemodialysis treatment. Methods: We obtained …

Introduction We aimed to assess whether machine learning models are superior at
predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional …

Background and objectives Hemodialysis complications remain a critical threat among
dialysis patients. They result in sudden termination of the session which impacts the …

The impact of the use of loop diuretics to prevent cumulative fluid balance in non‐oliguric
patients is uncertain. This is a retrospective study to estimate the association of time …

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or
drugs that adversely affect kidney function, is one that has yet to be explored in the critical …

Purpose Our objectives were to (1) employ ensemble machine learning algorithms utilizing
real-world clinical data to predict 90-day prognosis, including dialysis dependence and …

Methods We used two datasets-the first dataset (dev-dataset) for developing machine
learning models from the SEA-AKI study (n= 4,668) and the second (n= 357) for the external …

Background Recent studies suggest machine learning represents a promising predictive
option for patients in intensive care units (ICU). However, the machine learning performance …

Prior studies have used vital signs and laboratory measurements with conventional
modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to …

Purpose: Early prediction of Renal Replacement Therapy (RRT) requirements in intensive
care units (ICUs) has the potential to improve patient outcomes and resource allocation. This …