Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

• Published in: NPJ digital medicine Vol. 4; no. 1; p. 109
• Main Authors: Deng, Yixiang, Lu, Lu, Aponte, Laura, Angelidi, Angeliki M, Novak, Vera, Karniadakis, George Em, Mantzoros, Christos S
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 14.07.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Datasets; Diabetes; Digital technology; Glucose; Medical technology

Accurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity...