Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection

This study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. Existing studies typically rely on fully supervised machine learning, which presents challenges such as the labor-intensive manual process of annotating extensive training data for each disease and the need to design specialized deep learning architectures for each problem. To address such challenges, we propose a novel framework that leverages advanced AI techniques, including LLMs and multi-prompt engineering. Specifically, we address two key technical challenges in data-driven chronic disease management: (1) developing personalized prompts to represent each user's uniqueness and (2) incorporating medical knowledge into prompts to provide context for chronic disease detection, instruct learning objectives, and operationalize prediction goals. We evaluate our method using four mental disorders, which are prevalent chronic diseases worldwide, as research cases. On the depression detection task, our method (F1 = 0.975~0.978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0.760) and architecture engineering (F1 = 0.756). Meanwhile, our approach demonstrates success in few-shot learning, i.e., requiring only a minimal number of training examples to detect chronic diseases based on user-generated textual content (i.e., only 2, 10, or 100 subjects). Moreover, our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978).