- Title
- Machine learning in mental health: a scoping review of methods and applications
- Creator
- Shatte, Adrian; Hutchinson, Delyse; Teague, Samantha
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185550
- Identifier
- vital:16715
- Identifier
-
https://doi.org/10.1017/S0033291719000151
- Identifier
- ISBN:0033-2917
- Abstract
- 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. 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. 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. 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.
- Publisher
- Cambridge, UK: Cambridge University Press
- Relation
- Psychological Medicine Vol. 49, no. 9 (2019), p. 1426-1448
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Cambridge
- Subject
- Algorithms; Alzheimer's disease; Application; Artificial intelligence; Big Data; Bioinformatics; Clinical research; Clustering; Datasets; Diagnosis; Health psychology; Humans; Information technology; Learning; Learning algorithms; Machine Learning; Medical diagnosis; Medical prognosis; Medical research; Medical technology; Mental disorders; Mental Disorders - diagnosis; Mental Disorders - therapy; Mental Health; Natural language processing; Neural networks; Neurodegenerative diseases; Public Health; Researchers; Reviews; Schizophrenia; Social networks; Systematic review; Trees; 4905 Statistics; 5201 Applied and developmental pyschology; 5205 Social and personality pyschology
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