Early detection of sepsis using artificial intelligence: a scoping review protocol.

Journal: Systematic reviews
Published Date: Jan 16, 2021

Abstract

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.

Authors

  • Ivana Pepic
    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
  • Robert Feldt
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
  • Lars Ljungström
    Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
  • Richard Torkar
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
  • Daniel Dalevi
    Aweria AB, Gothenburg, 411 18, Sweden.
  • Hanna Maurin Söderholm
    PreHospen Centre for Prehospital Research, University of Borås, Borås, 50 190, Sweden.
  • Lars-Magnus Andersson
    Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
  • Marina Axelson-Fisk
    Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
  • Katarina Bohm
    Karolinska Institute, Department of Clinical Science and Education, South General Hospital, Stockholm, Sweden.
  • Bengt Arne Sjöqvist
    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
  • Stefan Candefjord
    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden. stefan.candefjord@chalmers.se.

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