Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

Journal: Computers in biology and medicine
Published Date:

Abstract

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.

Authors

  • M Hasan
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom. Electronic address: m.hasan@sheffield.ac.uk.
  • P A Bath
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom; The University of Sheffield, Information School, Sheffield, United Kingdom.
  • C Marincowitz
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • L Sutton
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • R Pilbery
    Yorkshire Ambulance Service NHS Trust, Research and Development, Wakefield, United Kingdom.
  • F Hopfgartner
    The University of Koblenz and Landau, Institute for Web Science and Technologies, Koblenz, Germany.
  • S Mazumdar
    The University of Sheffield, Information School, Sheffield, United Kingdom.
  • R Campbell
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • T Stone
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • B Thomas
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • F Bell
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • J Turner
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • K Biggs
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • J Petrie
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.
  • S Goodacre
    The University of Sheffield, School of Health and Related Research (ScHARR), Sheffield, United Kingdom.