Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.

Critical Care Infectious Disease Emergency Medicine Geriatrics Hospital-Based Medicine
Journal: Pediatric research
Published Date: Jun 19, 2025

Abstract

BACKGROUND: Early sepsis diagnosis is essential for initiating prompt treatment, preventing the progression of organ failure, and improving the survival rate of critically ill children. The aim of this study was to develop and validate a machine learning sepsis prediction model for patients admitted to a pediatric intensive care unit (PICU) who met the Phoenix Sepsis Score Criteria using EMR data.

Authors

  • Daniela Chanci
    Department of Biomedical Engineering, Duke University, Durham, North Carolina.
  • Jocelyn R Grunwell
    Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
  • Alireza Rafiei
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: [email protected].
  • Stephanie R Brown
    Section of Pediatric Critical Care, Oklahoma Children's Hospital and Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK.
  • Michael J Ripple
  • Natalie R Bishop
  • Prakadeshwari Rajapreyar
    Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
  • Lisa M Lima
    Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
  • Rishikesan Kamaleswaran
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.

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