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To lessen the time pediatric patients spend experiencing intraoperative hypoxemia, clinicians consider rapid identification and early intervention as crucial. Our research focused on the development and validation of a machine-learning model which anticipates intraoperative hypoxemic events in children under general anesthesia, with a one-minute prediction lead. A tertiary academic hospital's retrospe