一个经过调整的大型语言模型在糖尿病护理中促进了多项医疗任务。

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

糖尿病是一种慢性疾病，对全球健康构成重大负担，优化糖尿病管理需要多利益相关者的合作。大型语言模型（LLMs）在各种医疗场景中显示出潜力，但它们在各种糖尿病任务中的有效性尚未得到证实。在这项研究中，我们引入了一个框架来训练和验证糖尿病特定的LLMs。我们首先开发了一个全面的数据处理流程，包括数据收集、过滤、增强和精炼。这种方法有助于创建一个高质量的糖尿病特定数据集，并从头开始建立了几个评估基准。利用收集的训练数据集，我们对糖尿病特定的LLM系列进行了微调，相比其他LLMs，在理解和处理各种糖尿病任务方面展示了最先进的能力。此外，临床研究显示了我们模型在糖尿病护理中的潜在应用，包括提供个性化医疗、协助医学教育和简化临床任务。总之，我们的研究介绍了一个框架来开发和评估糖尿病特定的LLM系列，并强调了它在增强临床实践和为不同最终用户提供个性化、数据驱动的糖尿病支持方面的潜力。代码可通过GitHub获取：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.