Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation

The XGBoost and Transformer models demonstrated robust performance in predicting intraoperative hypoxemia in pediatric patients under general anesthesia across two hospitals. Adjustments for age-related variations did not enhance model performance. Future research should focus on developing machine learning models that can accurately distinguish true hypoxemia, leading to clinically significant improvements in patient outcomes.