连续血糖监测数据分析2.0：功能数据模式识别与人工智能应用
CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.
作者信息
Klonoff David C, Bergenstal Richard M, Cengiz Eda, Clements Mark A, Espes Daniel, Espinoza Juan, Kerr David, Kovatchev Boris, Maahs David M, Mader Julia K, Mathioudakis Nestoras, Metwally Ahmed A, Shah Shahid N, Sheng Bin, Snyder Michael P, Umpierrez Guillermo, Shao Mandy M, Scheideman Agatha F, Ayers Alessandra T, Ho Cindy N, Healey Elizabeth
机构信息
Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA.
International Diabetes Center at Park Nicollet, Minneapolis, MN, USA.
出版信息
J Diabetes Sci Technol. 2025 Aug 14:19322968251353228. doi: 10.1177/19322968251353228.
New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.0), these new methods, which we refer to as CGM Data Analysis 2.0, can provide a more detailed understanding of glucose fluctuations and trends and enable more personalized and effective diabetes management strategies once translated into practical clinical solutions.
连续血糖监测（CGM）数据分析的新方法正在涌现，这些方法对于解读CGM模式和潜在的代谢生理学具有重要价值。这些新方法使用功能数据分析和人工智能（AI），包括机器学习（ML）。与评估CGM追踪结果的传统指标（CGM数据分析1.0）相比，这些我们称为CGM数据分析2.0的新方法，一旦转化为实际临床解决方案，能够更详细地了解血糖波动和趋势，并实现更个性化、有效的糖尿病管理策略。
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