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

Linardon, Jake; Fuller-Tyszkiewicz, Matthew; Shatte, Adrian; Greenwood, Christopher J

Linardon, Jake; Fuller-Tyszkiewicz, Matthew; Shatte, Adrian; Greenwood, Christopher J.

Afiliación

Linardon J; Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, Victoria.
Fuller-Tyszkiewicz M; Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, Victoria.
Shatte A; Federation University, School of Engineering, Information Technology & Physical Sciences, Melbourne, Victoria, Australia.
Greenwood CJ; Centre for Social and Early Emotional Development and School of Psychology, Deakin University, Geelong, Victoria.

Int J Eat Disord ; 55(6): 845-850, 2022 06.

Article en En | MEDLINE | ID: mdl-35560256

Asunto(s)

Trastornos de Alimentación y de la Ingestión de Alimentos; Aprendizaje Automático; Trastornos de Ansiedad; Trastornos de Alimentación y de la Ingestión de Alimentos/diagnóstico; Trastornos de Alimentación y de la Ingestión de Alimentos/terapia; Humanos; Pronóstico

Palabras clave

adherence; digital; e-health; eating disorders; engagement; intervention; machine learning; prediction; randomized controlled trial; uptake

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Trastornos de Alimentación y de la Ingestión de Alimentos / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Int J Eat Disord Año: 2022 Tipo del documento: Article

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