ArticleFrontiers in pediatrics2025
Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model.
Article in Frontiers in pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
- Utilizing Machine Learning for Diagnostic Assistance of Pediatric Sepsis and Septic Shock in Resource-Limited Settings.Pediatric reports · 2026Article
- Early laboratory-based differentiation of severe influenza pneumonia and influenza-associated encephalopathy in children using a multi-model, stability-aware machine-learning workflow: a single-center retrospective cohort study.BMC infectious diseases · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Timely 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. Methods: This retrospective study included pediatric patients from Guangzhou Women and Children's Medical Center (GWCMC; Results: The 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 Conclusions: Our 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.
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Registered trials
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