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

By Bing Xue, Dingwen Li, Chenyang Lu, C. King, T. Wildes, M. Avidan, T. Kannampallil, Joanna Abraham. JAMA Network Open, 2021

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Key Points Question Can machine learning models predict patient risks of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, delirium, and pulmonary embolism? Findings In a cohort study of 111 888 operations at a large academic medical center, machine learning algorithms exhibited high areas under the receiver operating characteristic curve for predicting the risk of postoperative complications related to pneumonia, acute kidney injury, deep vein thrombosis, pulmonary embolism, and delirium. Meaning These findings suggest that machine learning models using preoperative and intraoperative data can predict postoperative complications and generate reliable and clinically meaningful interpretations for supporting clinical decisions along the perioperative care continuum.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Health Informatics

View Paper: https://www.semanticscholar.org/paper/685a5868b9953717f5f4089dfc64b855a53f47bb