Does an optimized machine learning model improve the prediction of major complications in patients undergoing cytoreductive surgery compared to traditional methods?
An explainable machine learning model outperforms traditional methods in predicting major complications after cytoreductive surgery and identifies distinct surgical risk phenotypes.
In this prognostic study using data from patients undergoing CRS, an optimized machine learning model demonstrated a superior ability to predict individual- and cohort-level risk of major complications vs traditional methods. Using the SHAP method, 6 distinct surgical phenotypes were identified based on sources of risk of major complications.
Deng et al. (Wed,) studied this question.