Comparative analysis develops predictions for acute kidney injury in patients with acute-on-chronic liver failure, suggesting ML can improve outcomes.
Background Acute kidney injury (AKI) is one of the serious complications in acute-on-chronic liver failure (ACLF), and the mortality rate is very high. Early identification of high-risk patients is critical. Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms.
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Zhang et al. (2025) studied this question.