This paper is in the following e-collection/theme issue:

Glucose Tracking and Self-Monitoring of Blood Glucose (152) Diabetes Self-Management (464) Machine Learning (1400)

Published on in Vol 6, No 2 (2021): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26909, first published .
Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Authors of this article:

Darpit Dave1 Author Orcid Image ;   Madhav Erraguntla1 Author Orcid Image ;   Mark Lawley1 Author Orcid Image ;   Daniel DeSalvo2 Author Orcid Image ;   Balakrishna Haridas3 Author Orcid Image ;   Siripoom McKay2 Author Orcid Image ;   Chester Koh4, 5 Author Orcid Image
Article Authors Cited by (16) Tweetations (1) Metrics

Darpit Dave   1 , MS ;   Madhav Erraguntla   1 , PhD ;   Mark Lawley   1 , PhD ;   Daniel DeSalvo   2 , MD ;   Balakrishna Haridas   3 , PhD ;   Siripoom McKay   2 , MD ;   Chester Koh   4, 5 , MD

1 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States

2 Department of Pediatrics, Baylor College of Medicine / Texas Children's Hospital, Houston, TX, United States

3 Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States

4 Division of Pediatric Urology, Texas Children's Hospital, Houston, TX, United States

5 Scott Department of Urology, Baylor College of Medicine, Houston, TX, United States

Corresponding Author:

  • Madhav Erraguntla, PhD
  • Department of Industrial and Systems Engineering
  • Texas A&M University
  • 4021 Emerging Technologies Building
  • College Station, TX, 77843
  • United States
  • Phone: 1 979-458-2336
  • Email: merraguntla@tamu.edu