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Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments

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  • Bongjin Lee
  • Hyun Jung Chung
  • Hyun Mi Kang
  • Do Kyun Kim
  • Young Ho Kwak

Abstract

Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as ‘Refined Lab-score’ or ‘clinical prediction rule’ have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children

Suggested Citation

  • Bongjin Lee & Hyun Jung Chung & Hyun Mi Kang & Do Kyun Kim & Young Ho Kwak, 2022. "Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0265500
    DOI: 10.1371/journal.pone.0265500
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