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
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This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
