Yangliu Guo

Abstract Early sepsis diagnosis in children remains challenging due to nonspecific presentations. This study aimed to develop an interpretable machine learning (ML) model to improve early prediction. We conducted a retrospective cohort study of pediatric patients with infections. Using clinical and cytokine data, key predictors were selected to construct and compare several machine learning models. The logistic regression model demonstrated the best overall performance for early sepsis prediction.