Few-Shot Learning for Chronic Disease Management: Leveraging Large
  Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
Haoxin Liu Wenli Zhang Jiaheng Xie Buomsoo Kim Zhu Zhang Yidong Chai
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 large language models 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).