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Articles were then synthesised via narrative review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0033291719000151_sec_a3\" sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0033291719000151_sec_a4\" sec-type=\"conclusion\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s0033291719000151","type":"journal-article","created":{"date-parts":[[2019,2,12]],"date-time":"2019-02-12T03:51:37Z","timestamp":1549943497000},"page":"1426-1448","source":"Crossref","is-referenced-by-count":973,"title":["Machine learning in mental health: a scoping review of methods and applications"],"prefix":"10.1017","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6225-9697","authenticated-orcid":false,"given":"Adrian B. 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