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

Abstract

The recent breakthrough in Artificial Intelligence (AI) has resulted in a profound impact on various domains including healthcare. Among them, this study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. We propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. 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). Our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978). In addition to the technical contributions, our proposed framework has the potential to improve the well-being of patients, control costs, and establish a more efficient and accessible healthcare system.