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Machine learning in mental health: a scoping review of methods and applications

Published online by Cambridge University Press:  12 February 2019

Adrian B. R. Shatte Open the ORCID record for Adrian B. R. Shatte [Opens in a new window] ,
Delyse M. Hutchinson Open the ORCID record for Delyse M. Hutchinson [Opens in a new window]  and
Samantha J. Teague Open the ORCID record for Samantha J. Teague [Opens in a new window]
Adrian B. R. Shatte*
Affiliation:
Federation University, School of Science, Engineering & Information Technology, Melbourne, Australia Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
Delyse M. Hutchinson
Affiliation:
Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia Murdoch Children's Research Institute, Centre for Adolescent Health, Royal Children's Hospital, Melbourne, Australia Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Melbourne, Australia University of New South Wales, National Drug and Alcohol Research Centre, Sydney, Australia
Samantha J. Teague
Affiliation:
Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
*
Author for correspondence: Adrian B. R. Shatte, E-mail: a.shatte@federation.edu.au
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Abstract

Background

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

Methods

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

Results

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

Conclusions

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Keywords

Big datahealth informaticsmachine learningmental health
Type
Review Article
Information
Psychological Medicine , Volume 49 , Issue 9 , July 2019 , pp. 1426 - 1448
Copyright
Copyright © Cambridge University Press 2019 

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