We have read the study titled “Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders” by Lou et al. and the accompanying editorial titled “Moving from ‘Surgeries’ to Patients: Progress and Pitfalls While Using Machine Learning to Personalize Transfusion Prediction” by Mathis et al. The authors include 4 million surgical cases during a 3-yr period from the American College of Surgeons National Surgical Quality Improvement Program database. The authors used the American College of Surgeons National Surgical Quality Improvement Program database to develop a machine learning model that incorporates patient- and surgery-specific variables to predict transfusion risk and the associated need for preoperative type and screen. The authors hypothesize that their machine learning algorithm would outperform the traditional approach of relying primarily on historical surgery-specific transfusion rates and thus optimize resource allocation by decreasing blood bank waste. The machine learning algorithm...

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