Machine learning models enhance triage for abdominal pain surgery

Machine learning models significantly outperform traditional logistic regression in predicting emergency surgery for abdominal pain patients. An analysis of 38,214 individuals identified 4,208 requiring emergency surgical intervention. Key results showed that the Light GBM model achieved an area under the curve (AUC) of 0.899, improving clinical decision-making through enhanced sensitivity and specificity. Overall, these advancements ensure timely treatment prioritization for critically ill patients, demonstrating the value of modern machine learning in emergency department triage.