Early Detection and Personalized Intervention in Mental Health

This research addresses the widespread issue of individuals adapting to stress without recognizing the potential long-term health consequences. To mitigate this, we propose a comprehensive solution that leverages self-reported mental health data. Our approach integrates a rule-based system with Natural Language Processing (NLP) techniques to offer personalized, structured support for users experiencing anxiety, stress, or negative thought patterns. The rule-based system identifies common cognitive distortions and applies predefined strategies to challenge and reframe these distorted thoughts. Furthermore, the system classifies the user's input to identify the type of mental health concern that can be addressed through therapeutic intervention. Additionally, a fine-tuned GPT-2 model generates dynamic, contextually appropriate responses aligned with Cognitive Behavioral Therapy (CBT) principles. When necessary, the system suggests specific activities and exercises within the chat to further support users. This dual approach-combining classification and real-time support-provides tailored interventions that adapt to individual needs and promote healthier coping mechanisms. We discuss the potential impact of this system on mental health management, particularly in enhancing self-awareness and fostering more effective, accessible therapeutic solutions through conversational AI.