Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.

Journal: Addiction (Abingdon, England)
Published Date: Nov 1, 2020

Abstract

BACKGROUND AND AIMS: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression.

Authors

  • Martyn Symons
    Telethon Kids Institute, University of Western Australia, Perth.
  • Gerald F X Feeney
    Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Centre for Youth Substance Abuse Research, The University of Queensland, Upland Road, St Lucia, Brisbane, Queensland 4072, Australia.
  • Marcus R Gallagher
    School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia.
  • Ross McD Young
    Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Faculty of Health, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland 4059, Australia.
  • Jason P Connor
    Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Wooloongabba, Brisbane, Queensland 4102, Australia; Discipline of Psychiatry, The University of Queensland, K Floor, Mental Health Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, Queensland 4029, Australia; Centre for Youth Substance Abuse Research, The University of Queensland, Upland Road, St Lucia, Brisbane, Queensland 4072, Australia. Electronic address: Jason.Connor@uq.edu.au.

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