Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning.

Journal: Pediatric research
PMID: 30928997

Abstract

BACKGROUND: Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.

Authors

  • Imant Daunhawer
    Adaptive Systems and Medical Data Science, Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
  • Severin Kasser
    Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Gilbert Koch
    Division of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Lea Sieber
    Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Hatice Cakal
    Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Janina Tütsch
    Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Marc Pfister
    Division of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
  • Sven Wellmann
    Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland. sven.wellmann@ukbb.ch.
  • Julia E Vogt

Keywords

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