Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study.

Journal: BMC emergency medicine
PMID: 39757181

Abstract

BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%. The aim was to develop a Machine Learning (ML) model to provide support in assessing patients in pre-hospital settings and to compare them with established triage tools.

Authors

  • Wivica Kauppi
    PreHospen- Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, SE- 501 90, Sweden. wivica.kauppi@hb.se.
  • Henrik Imberg
    Statistiska Konsultgruppen Sweden, Gothenburg, Sweden.
  • Johan Herlitz
    University of Gothenburg, Institute of Medicine, Sahlgrenska Academy, Gröna Stråket 4, 43146, Gothenburg, Sweden.
  • Oskar Molin
    Statistiska Konsultgruppen Sweden, Gothenburg, Sweden.
  • Christer Axelsson
    Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, Borås, Borås, Sweden.
  • Carl Magnusson
    PreHospen- Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, SE- 501 90, Sweden.

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