Using Machine Learning to Predict Perfusionists’ Critical Decision-Making During Cardiac Surgery

A machine learning approach is used to build predictive models of perfusionists’ decision-making during critical situations that occur in the cardiopulmonary bypass phase of cardiac surgery. Results may inform future development of computerized clinical decision support tools to be embedded into the operating room, improving patient safety and surgical outcomes.