Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning.

Ramos, Lucas A; Blankers, Matthijs; van Wingen, Guido; de Bruijn, Tamara; Pauws, Steffen C; Goudriaan, Anneke E

Ramos, Lucas A; Blankers, Matthijs; van Wingen, Guido; de Bruijn, Tamara; Pauws, Steffen C; Goudriaan, Anneke E.

Afiliación

Ramos LA; Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands.
Blankers M; Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands.
van Wingen G; Arkin Mental Health Care, Amsterdam, Netherlands.
de Bruijn T; Trimbos Institute, The Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands.
Pauws SC; Department of Psychiatry, Amsterdam UMC, and Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, Netherlands.
Goudriaan AE; Jellinek Prevention, Amsterdam, Netherlands.

Front Psychol ; 12: 734633, 2021.

Article en En | MEDLINE | ID: mdl-34552539

Palabras clave

ATOD; CBT; Substance Use Disorder; addiction; eHealth; log data analysis; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Front Psychol Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Suiza

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