| Abstrakt: |
Mental health affects our daily lives, including our emotions, thoughts, relationships, and actions. Anxiety and depression can disrupt eating, sleeping, and attention. Stigma worsens these issues by discouraging people from seeking help. Depression is a widespread condition globally, with significant social and economic consequences. Early and accurate diagnosis is necessary for proper care. AI and NLP offer potential solutions for mental health challenges, such as detecting early signs, personalized therapy, improved access to services, data-driven insights, automation, and self-care tools. However, ethical concerns and interdisciplinary collaboration are essential for responsible implementation in mental health care. Overall this study provides insights to the AI and ML methods used for diagnosis and also proposes on enhancing the speed and accuracy of depression diagnosis using ML and NLP. Social media and clinical data are combined to create a comprehensive dataset. ML methods are used to identify patterns, specifically verbal cues and emotions, while NLP is used to identify linguistic indicators of depression for early intervention and real-time monitoring. The objective is to improve diagnosis accuracy and provide timely assistance to individuals at risk of depression, benefiting mental health patients and improving their quality of life. [ABSTRACT FROM AUTHOR] |
