Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text

Encoding CGM data into text could enhance the classification of CGM profiles and enable the use of well-established search engines with CGM data. Other potential applications include predictive modeling, anomaly detection, indexing, trend analysis, or future generative artificial intelligence applications for diabetes research and clinical practice.

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Publication Details

Journal
Journal of Diabetes Science and Technology
Published
2025-03-20
DOI
https://doi.org/10.1177/19322968251323913
Citations
1
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
1.29
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article

Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text

Tobore Igbe, Boris P. Kovatchev
1 citations
Journal of Diabetes Science and Technology
Diabetes Management and Research
1.29
article

Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text

Tobore Igbe, Boris P. Kovatchev
article en
1 citations

Abstract

Encoding CGM data into text could enhance the classification of CGM profiles and enable the use of well-established search engines with CGM data. Other potential applications include predictive modeling, anomaly detection, indexing, trend analysis, or future generative artificial intelligence applications for diabetes research and clinical practice.

Journal of Diabetes Science and Technology
University of Virginia (US)
Quality Education
Openalex Percentile: Top 21%
Diabetes Management and Research
1.29
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