Application of artificial intelligence models in the identification of severe scrub typhus

This retrospective study enrolled 492 patients with scrub typhus in Jiangmen from 2013 to 2025. Clinical and laboratory data were analyzed using univariate logistic regression and LASSO regression to identify risk factors for severe illness. Seven machine-learning models, including logistic regression, support vector machine, random forest, XGBoost, Naive Bayes, k-nearest neighbor, and decision tree, were constructed and externally validated. Variable importance was ranked using SHAP analysis. Mechanical ventilation represented the strongest predictor of severe disease, indicating that respiratory failure is the core indicator of disease progression. Elevated bilirubin, prolonged coagulation time, and thrombocytopenia were also closely associated with severe scrub typhus. XGBoost achieved the best predictive performance, with AUC values of 0.888 and 0.928 in the training and validation cohorts, respectively. Owing to limited positive cases, mortality prediction yielded lower AUCs (0.856 and 0.814). These findings demonstrate that artificial intelligence models can effectively stratify the severity of scrub typhus and present favorable potential in prognostic assessment.