CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications

David C. Klonoff

doi:10.1177/19322968251353228

2025-08-14

David C. Klonoff

Richard M. Bergenstal

Nestoras Mathioudakis

Guillermo E. Umpierrez

DOI：10.1177/19322968251353228

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摘要

En 中文

新兴的连续血糖监测（CGM）数据分析方法正在涌现，这些方法对于解读CGM模式及潜在的代谢生理学具有重要价值。这些新方法采用功能数据分析（FDA）和人工智能（AI），包括机器学习（ML）。与传统用于评估CGM追踪结果的方法（CGM数据分析1.0）相比，这些新方法，我们称之为CGM数据分析2.0，能够提供更详细的血糖波动和趋势理解，并在转化为实用临床解决方案后，实现更个性化且有效的糖尿病管理策略。

Keyword:

continuous glucose monitoring

functional data analysis

artificial intelligence

machine learning

diabetes management

期刊

Journal of Diabetes Science and Technology

IF:

3.7

论文数:

1.5K

被引数:

6.8K

机构

diabetes research institute, mills-peninsula medical center, san mateo, ca, usa

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论文数: 7

被引数: 0

boston children's hospital and harvard medical school

学者数:

210

论文数: 72

被引数: 0

Sutter Health

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论文数: 34

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emory university school of medicine

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1.7K

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被引数: 0

Uppsala University and Science for Life Laboratory

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论文数: 1

被引数: 0

google research, mountain view, ca, us

学者数:

论文数: 1

被引数: 0

netspective foundation, inc., silver spring, md, usa

学者数:

论文数: 1

被引数: 0

diabetes technology society, burlingame, ca, usa

学者数:

论文数: 7

被引数: 0

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