超血糖的LLM-Powered预测和行为治疗途径的发现从可穿戴设备和饮食
Abdullah Mamun1,2, Asiful Arefeen1, Susan B Racette1
1College of Health Solutions, Arizona State University, Phoenix, AZ 85054, USA.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
预测饭后高血糖是预防糖尿病的关键. 使用可穿戴数据的可解释AI工具GlucoLens估计血糖峰值,并提供个性化的建议,以有效管理血糖水平.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 代谢健康研究 代谢健康研究
背景情况:
- 餐后高血糖症,饭后血糖升高,是糖尿病进展的信号.
- 准确预测食后葡萄糖动态对于积极的健康管理至关重要.
- 了解生活方式对葡萄糖水平的影响,可以使个人能够预防高血糖症.
研究的目的:
- 开发一种可解释的机器学习解决方案,GlucoLens,用于预测食后葡萄糖曲线下的区域 (AUC).
- 用传感器数据,生活方式因素和最近的葡萄糖模式来估计食后AUC和高血糖风险.
- 为高血糖管理提供可解释的预测和可操作的建议.
主要方法:
- 使用来自可穿戴设备 (活动,葡萄糖监测) 的传感器驱动输入和多式联络数据 (食品/工作日志).
- 开发了一个可解释的AI模型,集成先进的数据处理,大型语言模型和机器学习.
- 在一项为期五周的临床试验中对该模型进行了评估,该试验对10名全职工作的成年人进行了评估.
主要成果:
- 葡萄糖获得了0.123.3的正常化根平均平方误差 (NRMSE).
- 该模型显示,与现有方法相比,其预测性能提高了16%.
- 高血糖预测准确率为79%,F1得分为0.749,包括对治疗建议的反事实解释.
结论:
- 开发的机器学习模型GlucoLens有效估计食后AUC并预测高血糖.
- 可解释的人工智能方法为葡萄糖管理提供了可解释的见解和个性化的建议.
- 该方法显示了可通用性和与临床理解的一致性,有助于预防糖尿病.
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