Mental disorders are a critical issue in modern society, yet it remains to be consistently neglected. The COVID19 pandemic has made it much more difficult to seek assistance when one needs it. People are feeling increasingly anxious and uncertain about their futures while being socially separated from their friends and relatives. As people continue to quarantine among the limitations imposed by governments, interaction between clinical therapists or social workers and those suffering from mental illness has gotten increasingly limited. Machine learning is a vital approach for allowing virtual analysis of many forms of textual, audio, and visual data for sentiment analysis and understanding the mental health of people utilizing numerous critical parameters in this situation. This chapter aims to provide a systematic review of the current literature investigating COVID-19's impact on mental well-being, as well as studies that explore machine learning and artificial intelligence techniques to detect and treat mental illnesses when traditional therapies are unavailable due to lockdown and social distancing norms imposed. The different machine learning algorithms and deep learning approaches utilized in earlier studies are thoroughly discussed in this chapter. Detailed explanation of the data sources utilized and a review of the types of features investigated in mental disorder identification are included as well. The study's major findings are thoroughly discussed. The obstacles of employing machine learning techniques in biomedical applications are explored, as well as possibilities to enhance and progress the discipline.
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics A Computational Approach 2022, Pages 1-51,