Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.

Authors

  • Elizabeth Ford
    Department of Primary Care and Public Health Brighton and Sussex Medical School Brighton UK.
  • Philip Rooney
    Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9RQ, England.
  • Seb Oliver
    Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9RQ, England.
  • Richard Hoile
    Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, England.
  • Peter Hurley
    Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9RQ, England.
  • Sube Banerjee
    Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, England.
  • Harm van Marwijk
    Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, England.
  • Jackie Cassell
    Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, England.