Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48340, first published .
Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Journals

  1. Chen X, Tao L, Wang Y. Association of dietary fiber intake with all-cause and cardiovascular mortality in diabetes and prediabetes. Diabetology & Metabolic Syndrome 2025;17(1) View
  2. Hu J, Ren L, Wang T, Yao P. Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients. Journal of Multidisciplinary Healthcare 2025;Volume 18:4643 View

Conference Proceedings

  1. Satter S, Kwon T, Kim K. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Non-Invasive Blood Glucose Estimation Based on Machine Learning Algorithms Using PPG Signals View
  2. Cui Y, Ma S, Wu X, Wang B. Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. An LSTM-Based Model for Non-invasive Blood Glucose Prediction Utilizing BVP Signals View
  3. Dere B, Ogunkorode A, Akpor O, Oniyide A, Dere A, Bello K. 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON). Mobile and Wearable Solutions for Monitoring Diabetes: A Systematic Review View