Researchgradient tree boostingsepsis predictionehr clinical data
Pediatric ED Models Predict Sepsis With High Accuracy
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8.2
Alpern et al. (JAMA Pediatrics, 2025) developed and validated machine-learning models using electronic health record data from five PECARN health systems to predict pediatric sepsis with organ dysfunction within 48 hours, using the first four hours of ED data. The gradient tree boosting model achieved AUROC 0.94 and positive likelihood ratios up to 6.18, indicating strong discrimination and potential for earlier identification.
Scoring Rationale
High novelty and robust multicentre validation support impact, tempered by implementation challenges, fairness differences, and limited generalisability to other ED settings.
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