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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.07885 |
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| _version_ | 1866913834033217536 |
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| author | 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 Ayers, Alessandra T. Ho, Cindy N. Healey, Elizabeth |
| author_facet | 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 Ayers, Alessandra T. Ho, Cindy N. Healey, Elizabeth |
| contents | New methods of 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications 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 Ayers, Alessandra T. Ho, Cindy N. Healey, Elizabeth Quantitative Methods New methods of 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. |
| title | CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2505.07885 |