Automated Assistance for Mental Health using Deep Learning Approach

The prevalence of mental health issues is a growing concern, with approximately 1 in 4 people struggled with mental health issues at some time in their lives. While mental health support is available, many individuals face barriers to accessing traditional forms of treatment, such as long wait times, stigma, and financial constraints. As a result, there is a need for innovative solutions that can provide effective and accessible support to individuals experiencing mental health challenges. The research work proposes the development of an automated assistance system for mental health that leverages natural language processing and machine learning techniques to provide personalized and responsive support to individuals in need. The system will be designed to be interactive and conversational, using chatbot technology to engage with users in a natural and empathetic manner. To ensure the effectiveness and reliability of the system, this research work will adopt a user-cantered approach that involves extensive user testing and feedback. This approach will allow the system to be tailored to the needs and preferences of different users, including factors such as cultural background, language proficiency, and individual coping styles. The ultimate goal of this work is to create an automated assistance system that can serve as a reliable and accessible tool for individuals experiencing mental health issues. The system has the potential to help users better manage their mental health, access support when they need it, and improve their overall quality of life. Additionally, the system could serve as a complement to traditional mental health treatment, providing ongoing support and guidance to individuals even after they have completed formal treatment programs.