Predicting patient deterioration with physiological data using AI: systematic review protocol.

Journal: BMJ health & care informatics
Published Date: Aug 5, 2025

Abstract

INTRODUCTION: The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has limited positive and negative predictive accuracy. There is a growing body of research demonstrating the effectiveness of artificial intelligence (AI) in predicting clinical deterioration, but there is limited evidence to show which aspect of AI is best suited to this task. This systematic review aims to establish which AI or machine learning algorithm is best suited to analysing physiological data sets to predict patient deterioration in a hospital setting.

Authors

  • Lynsey Threlfall
    Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • Cen Cong
    Translational and Clinical Research Institute, Depth AI Lab, Newcastle University, Newcastle upon Tyne, United Kingdom.
  • Victoria Riccalton
    Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
  • Edward Meinert
    Digitally Enabled PrevenTative Health Research Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.
  • Chris Plummer
    Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia.

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