Machine learning in mental health: a scoping review of methods and applications
By Adrian B. R. Shatte, D. Hutchinson, Samantha J. Teague. Psychological Medicine, 2019
763 citations recorded by the source corpus.
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.
Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/f3cd9095d9c0cf07384d39c0847903a2c922c87e
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In Experimental and Cognitive Psychology
In Social Psychology
Citations in the corpus
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review (2021, neutral): “Indeed, mental health care is also benefiting from the advancements in ML [27-29].”
- The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, background): “These include medical concerns, such as infectious diseases, obesity, and substance use, and mental-health conditions (Paul & Dredze, 2017; Perna et al., 2018; Shatte et al., 2019).”
- Supervised machine learning: A brief primer (2020, background): “, 2018), detect and predict of the risk and trajectory of symptoms (Shatte et al., 2019), and study treatment outcome and differential treatment response (Kessler et al.”