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Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders

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Abstract

Mental disorders are closely related to deficits in cognitive control. Such cognitive impairments may result in aberrations in mood, thinking, work, body functions, emotions, social engagements and general behaviour. Mental disorders may affect the phenotypic behaviour like eye movements, facial expressions and speech. Furthermore, a close association has been observed within mental disorders and physiological responses emanating from the brain, muscles, heart, eyes, skin, etc. Mental disorders disrupt higher cognitive function, social cognition, control of complex behaviours and regulation of emotion. Cognitive computation may help understand such disruptions for improved decision-making with the help of computers. This study presents a systematic literature review to promulgate state of art computational methods and technologies facilitating automated detection of mental disorders. For this survey, the relevant literature between 2010 and 2021 has been studied. Recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model were adopted for identification, screening, validating and inclusion of research literature. The self-diagnosis tools for detection of mental disorders like questionnaires and rating scales are inconsistent and static in nature. They cannot encompass the diversity of mental disorders, inter-individual variability and impact of emotional state of an individual. Furthermore, there are no standard baselines for mental disorders. This situation mandates a multi-faceted approach which may utilise data from physiological signals, behavioural patterns and even data obtained from various online portals like social media to efficiently and effectively detect the prevalence, type and severity of mental disorders.

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Singh, J., Hamid, M.A. Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cogn Comput 14, 2169–2186 (2022). https://doi.org/10.1007/s12559-022-10042-2

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