Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

Journal: Journal of affective disorders
Published Date: Dec 25, 2018

Abstract

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted prevention, and lessen the burden of depression. Machine learning is a rapidly expanding field, and such approaches offer powerful predictive abilities. We investigated the utility of a machine learning approach to predict the persistence of depressive symptoms in older adults.

Authors

  • Christopher M Hatton
    Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK. Electronic address: hych2@hyms.ac.uk.
  • Lewis W Paton
    Department of Health Sciences, University of York, UK. Electronic address: lewis.paton@york.ac.uk.
  • Dean McMillan
    Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK. Electronic address: dean.mcmillan@york.ac.uk.
  • James Cussens
    Department of Computer Science & York Centre for Complex Systems Analysis, University of York, UK. Electronic address: james.cussens@york.ac.uk.
  • Simon Gilbody
    Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK. Electronic address: simon.gilbody@york.ac.uk.
  • Paul A Tiffin
    Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK. Electronic address: paul.tiffin@york.ac.uk.

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