Illuminate a Novel Approach for Depression Detection with Explainable Analysis and Proactive Therapy using Prompt Engineering.

Traditional methods of depression detection on social media forums can classify whether a user is depressed, but they often lack the capacity for human-like explanations and interactions. This paper proposes a next-generation paradigm for depression detection and treatment strategies. This research employs three advanced Large Language Models (LLMs) - Generative Pretrained Transformer 4 (GPT - 4), Llama 2 chat, and Gemini, each fine-tuned using specially engineered prompts to effectively diagnose, explain, and suggest therapeutic interventions for depression. These prompts are designed to guide the models in analyzing textual data from clinical interviews and online forums, ensuring nuanced and context-aware responses. The study introduces a novel approach to prompt engineering, utilizing a few-shot prompting methodology for the Diagnosis and Explanation component. This technique is optimized to provide Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, (DSM-5) based analysis and explanation, enhancing the model’s ability to identify and articulate depressive symptoms accurately. For the Interaction and Learning aspect, the models engage in empathetic dialogue management, guided by resources from Psychology Database and a Cognitive Behavioral Therapy (CBT) guide. This facilitates meaningful interactions with individuals facing major depressive disorders, fostering a supportive and understanding environment. Furthermore, the research innovates in Case Conceptualization and Treatment, creating the Illuminate Database to guide the models in offering personalized therapy. This database is enriched with various CBT modules, encompassing case conceptualization, treatment planning, and therapeutic techniques. The models utilize this information to offer structured, actionable steps for addressing mental health issues. The quantitative analysis of the study highlights the effectiveness of these LLMs, demonstrated through metrics such as F1 scores, Precision, Recall, Cosine similarity, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) across different test sets. This comprehensive approach, blending cutting-edge AI with established psychological methodologies, illuminates new possibilities in mental health care, showcasing the potential of LLMs in revolutionizing diagnosis and treatment strategies.