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
- end stage renal disease
- glycemic control
- chronic kidney disease
- ejection fraction
- newly diagnosed
- big data
- prognostic factors
- peritoneal dialysis
- cardiovascular disease
- electronic health record
- insulin resistance
- artificial intelligence
- blood pressure
- patient reported outcomes
- deep learning
- skeletal muscle
- adipose tissue
- metabolic syndrome
- data analysis