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

Machine Learning (1971) Artificial Intelligence in Diabetes Care and Prevention (45) Diabetes Self-Management (565) Glucose Tracking and Self-Monitoring of Blood Glucose (195) Development and Evaluation of Research Methods, Instruments and Tools (698) Computer-Aided Diagnosis (224) Decision Support for Health Professionals (1468) Artificial Intelligence (1996) Clinical Information and Decision Making (1807)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56909, first published .
Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study

Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study

Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study

Authors of this article:

Jian Shao1 Author Orcid Image ;   Ying Pan2 Author Orcid Image ;   Wei-Bin Kou3 Author Orcid Image ;   Huyi Feng4 Author Orcid Image ;   Yu Zhao1 Author Orcid Image ;   Kaixin Zhou1 Author Orcid Image ;   Shao Zhong2 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Liu X, Zhang J. Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes. Journal of Multidisciplinary Healthcare 2024;Volume 17:5513 View
  2. Ayers A, Ho C, Kerr D, Cichosz S, Mathioudakis N, Wang M, Najafi B, Moon S, Pandey A, Klonoff D. Artificial Intelligence to Diagnose Complications of Diabetes. Journal of Diabetes Science and Technology 2025;19(1):246 View
  3. 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
  4. Lara-Abelenda F, Chushig-Muzo D, Wägner A, Tayefi M, Soguero-Ruiz C. Interpretable and multimodal fusion methodology to predict severe hypoglycemia in adults with type 1 diabetes. Engineering Applications of Artificial Intelligence 2025;144:110142 View
  5. Tseng M, Vyas K, Das A, Quamer W, Dave D, Erranguntla M, Villegas C, DeSalvo D, McKay S, Cote G, Gutierrez-Osuna R. Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles. Journal of Diabetes Science and Technology 2025 View
  6. Goumas G, Vlachothanasi E, Fradelos E, Mouliou D. Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives. Diagnostics 2025;15(8):1037 View
  7. Park S, Kim S, Rim D. Cyclic dual latent discovery for improved blood glucose prediction through patient–provider interaction modeling: a prediction study. Ewha Medical Journal 2025;48(2):e34 View
  8. Corrao S, Janić M, Maggio V, Rizzo M. Machine learning and deep learning in diabetology: revolutionizing diabetes care. Frontiers in Clinical Diabetes and Healthcare 2025;6 View
  9. Klonoff D, Bergenstal R, Cengiz E, Clements M, Espes D, Espinoza J, Kerr D, Kovatchev B, Maahs D, Mader J, Mathioudakis N, Metwally A, Shah S, Sheng B, Snyder M, Umpierrez G, Shao M, Scheideman A, Ayers A, Ho C, Healey E. CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications. Journal of Diabetes Science and Technology 2025 View
  10. Ghosh K, Chandra S, Ghosh S, Ghosh U. Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review. Cureus 2025 View

Books/Policy Documents

  1. Ghaffour J, Ahajjam S, Taqafi I, Ezzati A. Advances in Intelligent Systems and Digital Applications. View

Conference Proceedings

  1. Fera N, Procopio A, Cutruzzolà A, Zaffino P, Cortese N, Formoso G, Cosentino C, Irace C. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Mechanistic and Data-Driven Models for Predicting Glucose Levels during Gestation View
  2. Park J, Hoang Q, Ta J, Kang S, Sohn K, Jun S. Proceedings of the Great Lakes Symposium on VLSI 2025. MAPLE: Flexible-Precision Processing-In-Memory Architecture for Efficient On-Device ML View