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Article Abstract
Rapid etiological identification of Staphylococcus aureus (S. aureus) in spinal infections can be challenging, often delaying targeted therapy. We developed a machine learning model leveraging XGBoost to predict S. aureus etiology in spinal infections directly from routine laboratory indicators. The XGBoost model demonstrated superior predictive performance (AUC 0.812; 95%CI: 0.728-0.896) among four algorithms, with SHAP analysis identifying D-dimer, Monocyte Percentage, Albumin, and Alanine Aminotransferase as crucial predictors.
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