Article

Early Prediction of Acute Kidney Injury in Pediatric Intensive Care Unit Patients Using an XGBoost-Based Machine Learning Model: A Retrospective Study

2026-05-20

Abstract excerpt

<title>Abstract</title> <p>Background Acute kidney injury (AKI) is a frequent and serious complication in pediatric intensive care units (PICUs), and early detection is limited by delayed creatinine-based criteria. Machine learning (ML) using electronic health record (EHR) data may improve early risk prediction. Objective To develop and evaluate an explainable Extreme Gradient Boosting (XGBoost) model for early...

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Literature Corpus work: 308a64c2-af45-5cc7-91cb-a6e852273832
DOI: 10.21203/rs.3.rs-9644930/v1

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