Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model.

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; 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 multivari

SUBMITTER: Shi X 

PROVIDER: S-EPMC12550503 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature