The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.

Journal: Journal of diabetes science and technology
PMID: 38372235

Abstract

INTRODUCTION: Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.

Authors

  • Mansur Shomali
    WellDoc, Inc, Baltimore, MD, USA mshomali@welldocinc.com.
  • Shiping Liu
    Center for Health Information and Decision Systems, University of Maryland, College Park, MD, USA.
  • Abhimanyu Kumbara
    Welldoc, Inc., Columbia, MD, USA.
  • Anand Iyer
    Department of (Neuro) Pathology, Academic Medical Center, Amsterdam, The Netherlands.
  • Guodong Gordon Gao
    University of Maryland, College Park, MD, United States of America. Electronic address: ggao@rhsmith.umd.edu.

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