.png){kind=icon}
Return
CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications
.png){kind=icon}
OA
AI
DOI：10.1177/19322968251353228.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
continuous glucose monitoring
functional data analysis
artificial intelligence
machine learning
diabetes management
Journal
IF:
3.7
Papers:
1.5K
Citations:
6.8K
Organization
View more organizations
Cited Papers
ShareSaveShareSaveShareSaveShareSaveA pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data.png)
NATIONAL SCIENCE REVIEWOAAI
NATIONAL SCIENCE REVIEWIF17.1
OAAIShareSaveA case study on using a large language model to analyze continuous glucose monitoring dataSCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSave View more