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SHR Neuro Krebs Kardio Lipid Stoffw Microb

Klonoff, DC; Bergenstal, RM; Cengiz, E; Clements, MA; Espes, D; Espinoza, J; Kerr, D; Kovatchev, B; Maahs, DM; Mader, JK; Mathioudakis, N; Metwally, AA; Shah, SN; Sheng, B; Snyder, MP; Umpierrez, G; Shao, MM; Scheideman, AF; Ayers, AT; Ho, CN; Healey, E.
CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.
J DIABETES SCI TECHN. 2025; 19322968251353228 Doi: 10.1177/19322968251353228 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Co-Autor*innen der Med Uni Graz
Mader Julia
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Abstract:
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.

Find related publications in this database (Keywords)
artificial intelligence
machine learning
pattern analysis
CGM
diabetes
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