Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.

Journal: BMC medical research methodology
Published Date: Jan 28, 2025

Abstract

BACKGROUND: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.

Authors

  • Victoria Moglia
    School of Computing, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK. scvcm@leeds.ac.uk.
  • Owen Johnson
    School of Computing, University of Leeds, Leeds, UK.
  • Gordon Cook
    Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
  • Marc de Kamps
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; School of Computing, University of Leeds, Leeds, UK.
  • Lesley Smith
    Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.

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