Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model - Shi, Xuan; Wang, Xuying; Yang, Haomei; Fan, Xiaowei; Liu, Guangming; Song, Yongling; Peng, Qiuyan; Wang, Qiang; Sun, Xin; Ma, Wencheng; Li, Peiqing (Frontiers in Pediatrics, 2025)
BackgroundTimely identification of pediatric sepsis remains a critical challenge in emergency and intensive care settings due to the heterogeneous clinical presentations across age groups. Existing scoring systems often lack temporal resolution and interpretability. We aimed to develop a real-time, machine learning–based prediction framework integrating static and dynamic electronic health record (EHR) features to support early sepsis detection.MethodsThis retrospective study included pediatric patients from Guangzhou Women and Children's Medical Center (GWCMC; n = 1,697) and an external validation cohort from the MIMIC-III database (n = 827). Irregular time-series data were imputed using a correlation-enhanced continuous time-window histogram with multivariate Gaussian processes (CTWH + MGP). We compared the predictive performance of XGBoost and gated recurrent unit (GRU)-based RNN models over a 12-h window prior to clinical diagnosis. Model outputs were validated internally and externally using AUROC, AUPRC, and Youden index, with SHAP-based interpretability applied to identify key clinical features.ResultsThe CTWH + MGP-XGBoost model achieved the highest AUROC at diagnosis time (T = 0 h; AUROC = 0.915), while the GRU-based model demonstrated superior temporal stability across early windows. Top contributing features included lactate, white blood cell count, pH, and vasopressor use. External validation confirmed generalizability (MIMIC-III AUROC = 0.905). Simulation of real-time alerts showed a median lead time of 6.2 h before clinical diagnosis, with κ = 0.82 agreement against physician-confirmed cases.ConclusionsOur results suggest that a dual-model ensemble combining interpolation-based preprocessing and interpretable machine learning enables robust early sepsis detection in pediatric populations. The system supports integration into EHR platforms for real-time clinical alerts and may inform prospective trials and quality improvement initiatives.