Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912581524> ?p ?o ?g. }
- W2912581524 endingPage "1448" @default.
- W2912581524 startingPage "1426" @default.
- W2912581524 abstract "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." @default.
- W2912581524 created "2019-02-21" @default.
- W2912581524 creator A5029125958 @default.
- W2912581524 creator A5033402524 @default.
- W2912581524 creator A5087157479 @default.
- W2912581524 date "2019-02-12" @default.
- W2912581524 modified "2023-10-16" @default.
- W2912581524 title "Machine learning in mental health: a scoping review of methods and applications" @default.
- W2912581524 cites W1490796347 @default.
- W2912581524 cites W1528741131 @default.
- W2912581524 cites W1529527003 @default.
- W2912581524 cites W1569321962 @default.
- W2912581524 cites W1596419020 @default.
- W2912581524 cites W1617815717 @default.
- W2912581524 cites W1623227340 @default.
- W2912581524 cites W1680797894 @default.
- W2912581524 cites W1822255859 @default.
- W2912581524 cites W1901616594 @default.
- W2912581524 cites W1905519752 @default.
- W2912581524 cites W1906120590 @default.
- W2912581524 cites W1927417898 @default.
- W2912581524 cites W1952758786 @default.
- W2912581524 cites W1973486373 @default.
- W2912581524 cites W1973752844 @default.
- W2912581524 cites W1979019686 @default.
- W2912581524 cites W1979096973 @default.
- W2912581524 cites W1984910136 @default.
- W2912581524 cites W1986858673 @default.
- W2912581524 cites W1988122824 @default.
- W2912581524 cites W1991750181 @default.
- W2912581524 cites W1994694505 @default.
- W2912581524 cites W2002628922 @default.
- W2912581524 cites W2005342778 @default.
- W2912581524 cites W2006591500 @default.
- W2912581524 cites W2007307305 @default.
- W2912581524 cites W2007872832 @default.
- W2912581524 cites W2007913863 @default.
- W2912581524 cites W2012035409 @default.
- W2912581524 cites W2016557887 @default.
- W2912581524 cites W2018731632 @default.
- W2912581524 cites W2031367265 @default.
- W2912581524 cites W2032488641 @default.
- W2912581524 cites W2037363485 @default.
- W2912581524 cites W2038166296 @default.
- W2912581524 cites W2043962319 @default.
- W2912581524 cites W2056841881 @default.
- W2912581524 cites W2061585911 @default.
- W2912581524 cites W2061752069 @default.
- W2912581524 cites W2065105806 @default.
- W2912581524 cites W2068264290 @default.
- W2912581524 cites W2072959562 @default.
- W2912581524 cites W2075105655 @default.
- W2912581524 cites W2075950485 @default.
- W2912581524 cites W2076151421 @default.
- W2912581524 cites W2077086070 @default.
- W2912581524 cites W2079204732 @default.
- W2912581524 cites W2086139571 @default.
- W2912581524 cites W2089109585 @default.
- W2912581524 cites W2106986048 @default.
- W2912581524 cites W2113870592 @default.
- W2912581524 cites W2117304999 @default.
- W2912581524 cites W2120351640 @default.
- W2912581524 cites W2121417905 @default.
- W2912581524 cites W2121468917 @default.
- W2912581524 cites W2123735083 @default.
- W2912581524 cites W2124200703 @default.
- W2912581524 cites W2129587854 @default.
- W2912581524 cites W2133554278 @default.
- W2912581524 cites W2134676768 @default.
- W2912581524 cites W2136681395 @default.
- W2912581524 cites W2139031580 @default.
- W2912581524 cites W2143563908 @default.
- W2912581524 cites W2145256501 @default.
- W2912581524 cites W2145480979 @default.
- W2912581524 cites W2147085624 @default.
- W2912581524 cites W2149858560 @default.
- W2912581524 cites W2150180799 @default.
- W2912581524 cites W2151554678 @default.
- W2912581524 cites W2154608152 @default.
- W2912581524 cites W2154758450 @default.
- W2912581524 cites W2155607477 @default.
- W2912581524 cites W2159128662 @default.
- W2912581524 cites W2160311059 @default.
- W2912581524 cites W2160939744 @default.
- W2912581524 cites W2160976404 @default.
- W2912581524 cites W2162317278 @default.
- W2912581524 cites W2167579130 @default.
- W2912581524 cites W2167969178 @default.
- W2912581524 cites W2171469118 @default.
- W2912581524 cites W2177693422 @default.
- W2912581524 cites W2179333161 @default.
- W2912581524 cites W2208039604 @default.
- W2912581524 cites W2226243673 @default.
- W2912581524 cites W2235686152 @default.
- W2912581524 cites W2236698906 @default.
- W2912581524 cites W2255469161 @default.
- W2912581524 cites W2263292588 @default.
- W2912581524 cites W2270492750 @default.