Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

Journal: PloS one
PMID: 38701064

Abstract

BACKGROUND: The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation.

Authors

  • Hassan Farhat
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Ahmed Makhlouf
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Padarath Gangaram
    Faculty of Health Sciences, Durban University of Technology, Durban, South Africa.
  • Kawther El Aifa
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Ian Howland
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Fatma Babay Ep Rekik
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Cyrine Abid
    Laboratory of Screening Cellular and Molecular Process, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia.
  • Mohamed Chaker Khenissi
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Nicholas Castle
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Loua Al-Shaikh
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Moncef Khadhraoui
    Higher Institute of Biotechnology, University of Sfax, Sfax, Tunisia.
  • Imed Gargouri
    Faculty of Medicine, University of Sfax, Sfax, Tunisia.
  • James Laughton
    Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
  • Guillaume Alinier
    Hamad Medical Corporation Ambulance Service, Doha P.O. Box 3050, Qatar.

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