Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

Journal: International journal of medical informatics
Published Date: Apr 10, 2021

Abstract

BACKGROUND AND OBJECTIVES: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis.

Authors

  • Nehal Hassan
    School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: n.a.m.hassan2@newcastle.ac.uk.
  • Robert Slight
    Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK. Electronic address: bob.slight@nhs.net.
  • Daniel Weiand
    Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK. Electronic address: dweiand@nhs.net.
  • Akke Vellinga
    CARA Network, School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
  • Graham Morgan
  • Fathy Aboushareb
    Northumbria Healthcare NHS Foundation Trust, Rake Lane, North Shields, Tyne and Wear, NE29 8NH, UK. Electronic address: fathy.aboushareb@nhs.net.
  • Sarah P Slight
    School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: Sarah.Slight@newcastle.ac.uk.

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