Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology

Study applies explainable machine learning (SHAP) to 1,481 neonatal surgeries, achieving 0.72 AUC for mortality prediction with random forest model. Analysis reveals intraoperative vital signs as critical risk factors beyond traditional statistical markers, providing clinicians interpretable predictions at individual case level.