Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques.

Journal: International journal of laboratory hematology
PMID: 33949115

Abstract

INTRODUCTION: Early diagnosis and antibiotic administration are essential for reducing sepsis morbidity and mortality; however, diagnosis remains difficult due to complex pathogenesis and presentation. We created a machine learning model for bacterial sepsis identification in the neonatal intensive care unit (NICU) using hematological analyzer data.

Authors

  • Brian Huang
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Robin Wang
    Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Aaron J Masino
    Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, 3535 Market Street, Suite 1024, Philadelphia, PA, 19104, USA. masinoa@email.chop.edu.
  • Amrom E Obstfeld
    Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

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