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关于深度学习算法在1型糖尿病中预测血糖的系统审查
IEEE journal of biomedical and health informatics
|January 14, 2026
概括
深度学习 (DL) 模型显示出使用持续血糖监测 (CGM) 数据预测1型糖尿病 (T1D) 血糖 (BG) 水平的前景. 未来的研究应该整合可解释的AI (XAI),以提高临床可靠性和安全性.
科学领域:
- 内分泌学和新陈代谢学
- 人工智能在医学中的应用
- 生物医学数据科学 生物医学数据科学
背景情况:
- 1型糖尿病 (T1D) 的管理依赖于持续的葡萄糖监测 (CGM) 来获得实时血糖 (BG) 数据.
- 预测算法,特别是深度学习 (DL),利用CGM数据来预测未来的BG水平,帮助治疗干预.
- 需要对DL用于BG预测的应用进行全面审查,以指导临床采用.
研究的目的:
- 系统地审查深度学习应用程序的当前状态,以预测1型糖尿病患者的血糖.
- 根据数据集特征,输入,培训,架构和性能指标来评估DL模型.
- 确定基于DL的BG预测的挑战和未来研究方向.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 在PubMed,Scopus和Web of Science数据库中进行的搜索.
- 对26项精选研究的分析,重点是DL模型用于T1D中BG预测.
主要成果:
- 深度学习模型显示了使用CGM数据进行准确的BG预测的巨大潜力.
- 评估的研究在数据集特征,模型输入,架构和预测地平线方面有所不同.
- 关键的挑战包括确保临床使用的DL模型的生理忠实性和可解释性.
结论:
- 深度学习模型为T1D管理中的实时BG预测提供了一个有希望的方法.
- 可解释性AI (XAI) 集成对于提高模型可靠性,安全性和临床采用是至关重要的.
- 未来的研究应该专注于开发可解释和生理上健全的DL模型,用于T1D护理.
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