Abstract
The COVID-19 pandemic is causing monumental effects on mental wellbeing worldwide. Literature calls for action to avert an impending global mental health crisis. This chapter presents the challenge, and presents a new approach to avert the crisis in mental health. The objective is to design a solution by applying advances in Deep Learning literature. The advances in Transformer neural network enable pandemic scale screening, mental health diagnostics and counseling. A synthesis of Multimodal Deep Learning architecture to analyze thinking patterns is presented. The use of BERT for diagnosis is well established in the literature. By approaching the modeling of Cognitive Triad and Cognitive Behaviour Therapy with an Encoder-Decoder Transformer architecture, this chapter initiates future research by interested communities to avert the global mental health crisis.
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Rajagopal, A., Nirmala, V., Andrew, J., Arun, M.V., Piush, A. (2023). Prevention of Global Mental Health Crisis with Transformer Neural Networks. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_11
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