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Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications
Mills Peninsula Med Ctr, Diabet Res Inst, 100 South San Mateo Dr,Room 1165, San Mateo, CA 94401 USA..
Int Diabet Ctr Pk Nicollet, Minneapolis, MN USA..
Univ Calif San Francisco, Dept Pediat, San Francisco, CA USA..
Childrens Mercy Kansas City, Kansas City, MO USA..
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2025 (English)In: Journal of Diabetes Science and Technology, E-ISSN 1932-2968, Vol. 19, no 6, p. 1515-1527Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Sage Publications, 2025. Vol. 19, no 6, p. 1515-1527
Keywords [en]
artificial intelligence, machine learning, pattern analysis, CGM, diabetes
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:uu:diva-579098DOI: 10.1177/19322968251353228ISI: 001551534000001PubMedID: 40814224Scopus ID: 2-s2.0-105014211534OAI: oai:DiVA.org:uu-579098DiVA, id: diva2:2038396
Available from: 2026-02-13 Created: 2026-02-13 Last updated: 2026-02-13Bibliographically approved

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Espes, Daniel

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