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This study develops and validates a machine learning model integrating the Systemic Immune-Inflammation Index (SII) and Lactate to enhance early risk stratification in pediatric sepsis. The model demonstrated superior predictive performance for 28-day mortality, identifying critical prognostic factors and improving upon traditional scoring systems. The findings suggest that this approach can guide timely interventions for high-risk pediatric patients with sepsis.

Author

riayadav

Language

EN