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

Glucose Tracking and Self-Monitoring of Blood Glucose (166) Diabetes Self-Management (495) Machine Learning (1501)

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 (19) Tweetations (1) Metrics

Journals

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  2. Jahromi R, Zahed K, Sasangohar F, Erraguntla M, Mehta R, Qaraqe K. Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes. JMIR Diabetes 2023;8:e40990 View
  3. Aljihmani L, Kerdjidj O, Petrovski G, Erraguntla M, Sasangohar F, Mehta R, Qaraqe K. Hand tremor-based hypoglycemia detection and prediction in adolescents with type 1 diabetes. Biomedical Signal Processing and Control 2022;78:103869 View
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  8. Dave D, Vyas K, Branan K, McKay S, DeSalvo D, Gutierrez-Osuna R, Cote G, Erraguntla M. Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry. Journal of Diabetes Science and Technology 2024;18(2):351 View
  9. Tsichlaki S, Koumakis L, Tsiknakis M. Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review. JMIR Diabetes 2022;7(3):e34699 View
  10. Zale A, Abusamaan M, McGready J, Mathioudakis N. Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients. eClinicalMedicine 2022;44:101290 View
  11. Aljihmani L, Kerdjidj O, Petrovski G, Erraguntla M, Sasangohar F, Mehta R, Qaraqe K. Hand Tremor-Based Hypoglycemia Detection and Prediction in Adolescents with Type 1 Diabetes. SSRN Electronic Journal 2022 View
  12. Robinson R, Liday C, Lee S, Williams I, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care—Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023;2:e46487 View
  13. Husain K, Sarhan S, AlKhalifa H, Buhasan A, Moin A, Butler A. Dementia in Diabetes: The Role of Hypoglycemia. International Journal of Molecular Sciences 2023;24(12):9846 View
  14. Xu H, Yu H, Cheng Z, Mu C, Bao D, Li X, Xing Q. Development and validation of a prediction model for self‐reported hypoglycemia risk in patients with type 2 diabetes: A longitudinal cohort study. Journal of Diabetes Investigation 2024;15(4):468 View
  15. Sadeghi P, Karimi H, Lavafian A, Rashedi R, Samieefar N, Shafiekhani S, Rezaei N. Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective. Expert Review of Clinical Immunology 2024;20(10):1219 View
  16. Dave D, Vyas K, Cote G, Erraguntla M. Hypoglycemia and hyperglycemia detection using ECG: A multi-threshold based personalized fusion model. Biomedical Signal Processing and Control 2024;96:106569 View
  17. Kulzer B, Freckmann G, Ziegler R, Schnell O, Glatzer T, Heinemann L. Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring. Journal of Diabetes Science and Technology 2024;18(5):1052 View
  18. Herrero P, Andorrà M, Babion N, Bos H, Koehler M, Klopfenstein Y, Leppäaho E, Lustenberger P, Peak A, Ringemann C, Glatzer T. Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App. Journal of Diabetes Science and Technology 2024;18(5):1014 View
  19. Piersanti A, Morettini M, Cristino S, Giudice L, Burattini L, Mosconi G, Göbl C, Mambelli E, Tura A. Assessing hypoglycemia risk during hemodialysis using an explainable machine learning approach based on continuous glucose monitoring metrics. Biomedical Signal Processing and Control 2025;102:107319 View