一個適應性的大型語言模型有助於糖尿病護理中的多項醫療任務。

An adapted large language model facilitates multiple medical tasks in diabetes care

September 20, 2024

作者: Lai Wei, Zhen Ying, Muyang He, Yutong Chen, Qian Yang, Yanzhe Hong, Jiaping Lu, Xiaoying Li, Weiran Huang, Ying Chen

cs.AI

摘要

糖尿病是一種慢性疾病，對全球健康造成重大負擔，優化糖尿病管理需要多方合作。大型語言模型（LLMs）在各種醫療場景中顯示出潛力，但它們在各種糖尿病任務中的有效性尚未得到證實。在本研究中，我們介紹了一個框架來訓練和驗證糖尿病特定的LLMs。我們首先開發了一個包括數據收集、過濾、擴增和精煉的全面數據處理管道。這種方法有助於創建高質量的糖尿病特定數據集，以及從頭開始建立幾個評估基準。利用收集的訓練數據集，我們對糖尿病特定的LLM家族進行了微調，相較於其他LLMs，展示了在理解和處理各種糖尿病任務方面的最新專業知識。此外，臨床研究顯示了我們模型在糖尿病護理中的潛在應用，包括提供個性化醫療、協助醫學教育和簡化臨床任務。總之，我們的研究介紹了一個框架來開發和評估糖尿病特定的LLM家族，並突顯了它在增強臨床實踐和為面對不同最終用戶時提供個性化、數據驅動的糖尿病支持方面的潛力。代碼可通過GitHub提供，網址為https://github.com/waltonfuture/Diabetica。

English

Diabetes is a chronic disease that poses a significant global health burden, and optimizing diabetes management requires multi-stakeholder collaboration. Large language models (LLMs) have shown promise in various healthcare scenarios, but their effectiveness across a diverse range of diabetes tasks remains unproven. In this study, we introduced a framework to train and validate diabetes-specific LLMs. We first developed a comprehensive data processing pipeline that includes data collection, filtering, augmentation and refinement. This approach contributes to creating a high-quality, diabetes-specific dataset, and several evaluation benchmarks entirely from scratch. Utilizing the collected training dataset, we fine-tuned a diabetes-specific LLM family that demonstrated state-of-the-art proficiency in understanding and processing various diabetes tasks compared to other LLMs. Furthermore, clinical studies showed the potential applications of our models in diabetes care, including providing personalized healthcare, assisting medical education, and streamlining clinical tasks. In conclusion, our study introduced a framework to develop and evaluate a diabetes-specific LLM family, and highlighted its potential to enhance clinical practice and provide personalized, data-driven support for diabetes support when facing different end users. The code is provided via GitHub at https://github.com/waltonfuture/Diabetica.