Sepsis is a highly prevalent condition in intensive care units, with one of its severe
complications being acute kidney injury (AKI). Sepsis -associated acute kidney injury
(SA-AKI) can be a reversible process if timely recognition and adequate treatment
are provided to the patient. This systematic review (SR) summarizes the current clinical
evidence of machine learning (ML) based prediction models. After conducting the literature
search, 9 publications meet the inclusion criteria of the SR, categorized into three
groups: prediction of SAAKI occurrence, prediction of persistent AKI in septic patients,
and prediction of mortality in SA-AKI patients. In summary, based on the current clinical
evidence, ML -based methods show great potential for future clinical applications.
They have the ability to outperform conventional scoring systems (such as SOFA and
SAPS II), indicating their promising role in clinical practice.
