Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial.

Journal: JMIR mHealth and uHealth
PMID: 31682586

Abstract

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM.

Authors

  • Syed Hasib Akhter Faruqui
    Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, United States of America.
  • Yan Du
    State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China; School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China. Electronic address: duyan@ciac.ac.cn.
  • Rajitha Meka
    Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, United States.
  • Adel Alaeddini
    Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, Texas, United States.
  • Chengdong Li
    College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China. Electronic address: lichengdong@qust.edu.cn.
  • Sara Shirinkam
    Department of Mathematics and Statistics, University of the Incarnate Word, San Antonio, TX, United States.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.

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