A systematic review of networks for prognostic prediction of health outcomes and diagnostic prediction of health conditions within Electronic Health Records.

Journal: Artificial intelligence in medicine
Published Date: Oct 23, 2024

Abstract

BACKGROUND AND OBJECTIVE: Using graph theory, Electronic Health Records (EHRs) can be represented graphically to exploit the relational dependencies of the multiple information formats to improve Machine Learning (ML) prediction models. In this systematic qualitative review, we explore the question: How are graphs used on EHRs, to predict diagnosis and health outcomes?

Authors

  • Zoe Hancox
    University of Leeds, Leeds, United Kingdom. Electronic address: Z.L.Hancox@leeds.ac.uk.
  • Allan Pang
    University of Leeds, Leeds, United Kingdom; Royal Centre for Defence Medicine, Research & Clinical Innovation (RCI), ICT Centre, Vincent Drive, Birmingham, United Kingdom. Electronic address: allan.pang@nhs.net.
  • Philip G Conaghan
    Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, and NIHR Leeds Biomedical Research Centre, UK.
  • Sarah R Kingsbury
    Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, United Kingdom; NIHR Leeds Biomedical Research Centre, United Kingdom.
  • Andrew Clegg
    Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK.
  • Samuel D Relton
    Leeds Institute of Health Science, University of Leeds Leeds UK.

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