Advancing Early Pediatric Sepsis Detection: Machine Learning Models Predicting Onset Within 48 Hours

Highlight

• Machine learning models accurately predict pediatric sepsis risk within 48 hours of emergency department (ED) presentation using early clinical data.
• Models incorporating gradient tree boosting achieved AUROCs up to 0.94 for sepsis and 0.92 or greater for septic shock prediction.
• Key predictive features included emergency severity index, age-adjusted vital signs, and medical complexity extracted from EHR data in initial 4 hours of ED care.
• Fairness analysis showed consistent model performance across demographics, with higher accuracy in Medicaid-insured patients compared to those with commercial insurance.

Study Background