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Predicting the causative pathogen among children with pneumonia using a causal Bayesian network.

Yue WuSteven MascaroMejbah BhuiyanParveen FathimaAriel O MaceMark P NicolPeter C RichmondLea-Ann KirkhamMichael DymockDavid A FoleyCharlie McLeodMeredith L BorlandAndrew MartinPhoebe C M WilliamsJulie A MarshThomas L SnellingChristopher C Blyth
Published in: PLoS computational biology (2023)
To our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. We have shown how the method works and how it would help decision making on the use of antibiotics, providing insight into how computational model predictions may be translated to actionable decisions in practice. We discussed key next steps including external validation, adaptation and implementation. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.
Keyphrases
  • healthcare
  • primary care
  • decision making
  • emergency department
  • young adults
  • intensive care unit
  • candida albicans
  • quality improvement
  • health information
  • affordable care act