Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

Journal: European respiratory review : an official journal of the European Respiratory Society
PMID: 39537241

Abstract

BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.

Authors

  • Martina Votto
    Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Annalisa De Silvestri
    Biometry and Clinical Epidemiology, Scientific Direction, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
  • Lorenzo Postiglione
    Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Maria De Filippo
    Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Sara Manti
    Pediatric Unit, Department of Human Pathology in Adult and Developmental Age "Gaetano Barresi", University of Messina, Messina, Italy.
  • Stefania La Grutta
    Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, Palermo, Italy.
  • Gian Luigi Marseglia
    Department of Pediatrics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.
  • Amelia Licari
    Pediatric Clinic, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy. Electronic address: amelia.licari@unipv.it.

Keywords

External Resources

Popular Topics
Recent Journals