In contrast to traditional risk assessment methods (logistic regression), which assume that risk factors interact linearly and additively,
the non-linear machine learning methodology of Optimal Classification Trees provides superior power for predicting risks after
congenital heart surgery, with the advantage over other machine learning methods of logical interpretability. This methodology also
allows estimation of individual patient risk, based on aggregate database data, and may facilitate decision–making and quality
improvements in congenital heart surgery.
Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach
Source: World Journal for Pediatric and Congenital Heart Surgery
Source URL: https://doi.org/10.1177%2F21501351211007106
Keywords:
Author(s): Dimitris Bertsimas, PhD, Daisy Zhuo, PhD, Jack Dunn, PhD, Jordan Levine, MΕng, Eugenio Zuccarelli, MBAn, MSc, Nikos Smyrnakis
Nikos Smyrnakis, Zdzislaw Tobota, MD, Bohdan Maruszewski, MD, PhD, Jose Fragata, MD, PhD Jose Fragata, George E. Sarris , MD, PhD