Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification Trees by Bertsimas, Dimitris, Zhuo, Daisy, Levine, Jordan, Dunn, Jack, Tobota, Zdzislaw, Maruszewski, Bohdan, Fragata, Jose, Sarris, George E

“…Methods: The European Congenital Heart Surgeons Association Congenital Database data subset (31 792 patients) who had undergone any of the 10 “benchmark procedure group” primary procedures were analyzed. …”
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Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach by Bertsimas, Dimitris, Zhuo, Daisy, Dunn, Jack, Levine, Jordan, Zuccarelli, Eugenio, Smyrnakis, Nikos, Tobota, Zdzislaw, Maruszewski, Bohdan, Fragata, Jose, Sarris, George E

“…</jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p> We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. …”
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