用大型语言模型对1型糖尿病患者进行个性化葡萄糖预测
Francisco J Lara-Abelenda1, David Chushig-Muzo1, Pablo Peiro-Corbacho1
1Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain.
Computer methods and programs in biomedicine
|April 6, 2025
概括
大型语言模型在预测1型糖尿病管理中的葡萄糖水平方面表现有前途. 使用这些模型的个性化方法实现了最佳的短期葡萄糖预测结果.
科学领域:
- 人工智能的人工智能
- 内分泌学 在内分泌学.
- 生物医学信息学 生物医学信息学
背景情况:
- 1型糖尿病 (T1D) 的管理需要精确控制葡萄糖水平,使用外源胰岛素.
- 持续葡萄糖监测 (CGM) 已经推进了糖尿病治疗,但葡萄糖的变化仍然是一个挑战.
- 大型语言模型 (LLM) 在文本中很成功,但对于多式联络数据 (如葡萄糖预测) 尚未探索.
研究的目的:
- 评估基于LLM的模型,用于T1D中的葡萄糖预测.
- 将每日多次注射胰岛素 (MDIs) 和用户之间的预测性能进行比较.
- 开发一个个性化,自适应的葡萄糖预测模型.
主要方法:
- 利用T1DEXI研究中的CGM数据进行葡萄糖水平预测.
- 评估各种预测模型,包括基于LLM的方法.
- 使用60分钟,90分钟和120分钟预测地平线的平均绝对误差 (MAE) 评估模型性能.
主要成果:
- 个性化的LLM方法产生了优异的短期 (60-90分钟) 葡萄糖预测 (MAE:15.2-17.2,MDIs15.7-20.2).
- 对于长期 (120分钟) 预测,TIDE (MAE:19.8对于MDIs) 和Patch-TST (MAE:18.5) 显示了具有竞争力的结果.
- 在LLM模型中,可以将MAE与最先进的方法进行比较,但变化性降低.
结论:
- 基于LLM的模型为T1D中的葡萄糖预测提供了可行的替代方案.
- 个性化方法显示了改善短期血糖控制的巨大潜力.
- 这项研究推进了个性化预测模型,以缓解糖尿病并发症.
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