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

Shatte, Adrian B.R.;Hutchinson, Delyse M.;Teague, Samantha J.

Abstract

Objective: 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. Materials and Methods: Eight health and information technology research databases were searched using the terms “big data” or “machine learning” and “mental health”. Articles were assessed by two reviewers, and data were extracted on the article’s mental health application, ML technique, data type and size, 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. Discussion and Conclusion: 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 improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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Psychological Medicine

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1469-8978

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Cambridge University Press

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DOI

10.1017/S0033291719000151