Machine Learning Applications For The Promotion And Management Of Community Mental Health In The Pandemic Era Covid-19: A Scoping Review
Abstract
The Covid-19 pandemic affects not only physical health but also people's mental health. The machine learning approach has been widely used to support the policymaking related to handling Covid-19. However, its application in the handling of public mental health in the pandemic era has not been clearly conceptualized. This review aims to identify and map the innovations in the promotion and treatment of public mental health-based machine learning during pandemics. The systematic scoping review process is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The search is carried out in two stages. First, searches were conducted on five online databases: SpringerLink, Science Direct, ProQuest, Scopus, and Nature. In the next stage, the addition of literature with snowball techniques through manual searches from other resources, including Google Scholar. As much As 34 eligible literature has been selected and classified into three categories; the application purpose, the method used, and the performance result. The application of machine learning for the classification and prediction of mental health status results in an average accuracy above 80%. There are many variations of machine learning applications for mental health in pandemic times, both prevention and handling of mental health disorders. The addition of more neuro-psycho-physiological predictors based on objective assessments may support earlier assessment of vulnerable individuals, so that would be an important step forward in the prevention of mental health disorders caused by the pandemic.
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