Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

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Abstract
More than 10% of surgical patients experience major postoperative complications (eg, myocardial infarction, infection, and blood clots),1-3 leading to increased mortality, increased need for a higher level of care and management, increased length of postoperative hospital stay, and increased costs of care.4 Although some of these postoperative complications are unavoidable because of patient and surgical risk factors,5 others are modifiable and potentially preventable through early identification of patient risk factors and administration of evidence-based treatment approaches (eg, timely administration of antibiotics6,7 in postoperative care settings).8,9

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