An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.

Journal: The International journal of eating disorders
PMID: 35560256

Abstract

OBJECTIVE: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms.

Authors

  • Jake Linardon
    School of Psychology, Deakin University, Geelong, Victoria, Australia.
  • Matthew Fuller-Tyszkiewicz
    School of Psychology, Deakin University, Geelong, Victoria, Australia.
  • Adrian Shatte
    Federation University, School of Engineering, Information Technology & Physical Sciences, Melbourne, Victoria, Australia.
  • Christopher J Greenwood
    Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, Victoria.

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