Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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.
Recommended Citation
Liu, Haoxin; Zhang, Wenli; Xie, Jiaheng; Kim, Buomsoo; Zhang, Zhu; Chai, Yidong; and Ram, Sudha, "Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/cl/technological_advancements/3
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
Hilton Waikoloa Village, Hawaii
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.
https://aisel.aisnet.org/hicss-58/cl/technological_advancements/3