Using classification trees to identify psychotherapy patients at risk for poor treatment adherence-Reference-Cited by-同舟云学术

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Using classification trees to identify psychotherapy patients at risk for poor treatment adherence

Published:2023-03-07 Issue: Volume: Page:1-12
ISSN:1050-3307
Container-title:Psychotherapy Research
language:en
Short-container-title:Psychotherapy Research

Author:

Regan Timothy¹^ORCID,McCredie Morgan N.²^ORCID,Harris Bethany²^ORCID,Clark Shaunna³^ORCID

Affiliation:

1. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

2. Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA

3. Department of Psychiatry & Behavioral Sciences, Texas A&M College of Medicine, USA

Funder

NIDA

Publisher

Informa UK Limited

Subject

Clinical Psychology

Link

https://www.tandfonline.com/doi/pdf/10.1080/10503307.2023.2183911

Reference48 articles.

1. A scoping review of machine learning in psychotherapy research

2. Early withdrawal from mental health treatment: Implications for psychotherapy practice.

3. A Follow-up Study of Characteristics of Young People that Dropout and Continue Psychotherapy: Service Implications for a Clinic in the Community

4. Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms

5. Predicting dropout using DSM–5 Section II personality disorders, and DSM–5 Section III personality traits, in a (day)clinical sample of personality disorders.

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