打造专用于糖尿病管理的AI模型，提升诊疗效率与个性化服务
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
- 构建糖尿病专用数据集与评估基准，训练领域适配大模型
- 在多种糖尿病任务中表现优于通用大模型，实现精准医疗支持
- 适合临床医生、研究人员及糖尿病患者管理工具开发
糖尿病是一种全球负担沉重的慢性病，需多方协作进行有效管理。尽管大语言模型在医疗场景中展现潜力，但在多样化的糖尿病任务中效果尚未验证。本研究提出一个框架，用于训练和验证糖尿病专用大模型。我们首先建立了一套完整的数据处理流程，涵盖数据收集、筛选、增强与优化，从零构建高质量糖尿病专属数据集和评估基准。在所采集的训练数据上微调后，我们的糖尿病专用大模型家族在多项糖尿病任务中表现出领先水平，优于其他现有大模型。临床研究进一步揭示其在个性化健康管理、医学教育辅助及临床工作流优化中的应用潜力。总体而言，该框架为开发糖尿病专用大模型提供了方法论支持，凸显其在改善临床实践和为不同用户提供数据驱动式管理支持方面的前景。代码、基准与模型已开源：https://github.com/waltonfuture/Diabetica。
原文摘要 · Abstract (English)
Diabetes is a chronic disease with a significant global health burden, requiring multi-stakeholder collaboration for optimal management. Large language models (LLMs) have shown promise in various healthcare scenarios, but their effectiveness across diverse diabetes tasks remains unproven. Our study 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 created a high-quality, diabetes-specific dataset and evaluation benchmarks from scratch. Fine-tuned on the collected training dataset, our diabetes-specific LLM family demonstrated state-of-the-art proficiency in processing various diabetes tasks compared to other LLMs. Furthermore, clinical studies revealed the potential applications of our models in diabetes care, including providing personalized healthcare, assisting medical education, and streamlining clinical tasks. Generally, our introduced framework helps develop diabetes-specific LLMs and highlights their potential to enhance clinical practice and provide personalized, data-driven support for diabetes management across different end users. Our codes, benchmarks and models are available at https://github.com/waltonfuture/Diabetica.
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