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

journal contribution

posted on 2022-06-01, 00:00 authored by Jake LinardonJake Linardon, Matthew Fuller-TyszkiewiczMatthew Fuller-Tyszkiewicz, Adrian Shatte, Christopher GreenwoodChristopher Greenwood

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

History

Journal

International Journal of Eating Disorders

Volume

55

Issue

6

Pagination

845 - 850

Publisher

Wiley

Location

Chichester, Eng.

Publisher DOI

https://doi.org/10.1002/eat.23733

Link to full text

http://doi.org/10.1002/eat.23733

ISSN

0276-3478

eISSN

1098-108X

Language

eng

Author URL

https://www.ncbi.nlm.nih.gov/pubmed/35560256

Publication classification

C1 Refereed article in a scholarly journal

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Keywords

adherence digital eating disorders e-health engagement intervention machine learning prediction randomized controlled trial uptake Social Sciences Science & Technology Life Sciences & Biomedicine Psychology, Clinical Nutrition & Dietetics Psychiatry Psychology OUTCOMES

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All Rights Reserved

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