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ABSTRACT: Background
Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI.Objectives
This study tries to predict postoperative AKI using interpretable machine learning models.Methods
For this study, the information of 1435 patients was collected from multiple centers. The gathered data are in six categories: demographic characteristics and type of surgery, past medical history (PMH), drug history (DH), laboratory information, anesthesia and surgery information, and postoperati
SUBMITTER: Ejmalian A
PROVIDER: S-EPMC10016126 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature