A scoping review of machine learning in psychotherapy research

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of curr...

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Vydáno v:Psychotherapy research Ročník 31; číslo 1; s. 92 - 116
Hlavní autoři: Aafjes-van Doorn, Katie, Kamsteeg, Céline, Bate, Jordan, Aafjes, Marc
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Routledge 2021
Taylor & Francis Ltd
Témata:
artificial intelligence
big data
Clinical medicine
Data
Development studies
Feasibility
Humans
Machine Learning
Medical treatment
Psychotherapy
Psychotherapy research
Research methodology
scoping review
Systematic review
Treatment compliance
psychotherapy
big data
machine learning
artificial intelligence
scoping review
ISSN:1050-3307, 1468-4381, 1468-4381
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Shrnutí:Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review paper aims to broadly explore the nature of research activity using ML in the context of psychological talk therapies, highlighting the scope of current methods and considerations for clinical practice and directions for future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types of studies, those who developed and tested an ML model (k=44), and those who reported on the feasibility of a particular treatment tool that uses an ML algorithm (k=7). Most model development studies used supervised learning techniques to classify or predict labeled treatment process or outcome data, whereas others used unsupervised techniques to identify clusters in the unlabeled patient or treatment data. Overall, the current applications of ML in psychotherapy research demonstrated a range of possible benefits for indications of treatment process, adherence, therapist skills and treatment response prediction, as well as ways to accelerate research through automated behavioral or linguistic process coding. Given the novelty and potential of this research field, these proof-of-concept studies are encouraging, however, do not necessarily translate to improved clinical practice (yet).
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ISSN:1050-3307
1468-4381
1468-4381
DOI:10.1080/10503307.2020.1808729