Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study.
Nicholas Berin ChanWeizi LiTheingi AungEghosa BazuayeRosa M MonteroPublished in: JMIR AI (2023)
The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.
Keyphrases
- blood glucose
- type diabetes
- machine learning
- glycemic control
- end stage renal disease
- ejection fraction
- chronic kidney disease
- big data
- cardiovascular disease
- oxidative stress
- palliative care
- peritoneal dialysis
- prognostic factors
- electronic health record
- insulin resistance
- artificial intelligence
- deep learning
- patient reported outcomes
- skeletal muscle