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

Machine Learning (1770) Clinical Information and Decision Making (1688) Electronic Health Records (1198) Decision Support for Health Professionals (1361) Natural Language Processing (809) Artificial Intelligence (1779) Secondary Use of Clinical Data for Research and Surveillance (419) Diabetes-specific EHR Improvements (34)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36958, first published .
Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation

Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation

Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation

Authors of this article:

Hao Yang1 Author Orcid Image ;   Jiaxi Li2 Author Orcid Image ;   Siru Liu3 Author Orcid Image ;   Xiaoling Yang4 Author Orcid Image ;   Jialin Liu1, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (5) Metrics

Journals

  1. Liu S, Schlesinger J, McCoy A, Reese T, Steitz B, Russo E, Koh B, Wright A. New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record. Journal of the American Medical Informatics Association 2022;30(1):120 View
  2. Zhang L, Yang L, Zhou Z. Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice. Frontiers in Public Health 2023;11 View
  3. Wang S, Zhang Y, Sun F, Xi K, Sun Z, Zheng X, Guo F, Zhong H, Yang M, Shao Y, Huang B, Dong M, Ni S, Sun L. Catalase-like nanozymes combined with hydrogel to facilitate wound healing by improving the microenvironment of diabetic ulcers. Materials & Design 2023;225:111557 View
  4. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
  5. Agraz M, Deng Y, Karniadakis G, Mantzoros C. Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset. Scientific Reports 2024;14(1) View
  6. Huang Y, Guo J, Donahoo W, Lee Y, Fan Z, Lu Y, Chen W, Tang H, Bilello L, Saguil A, Rosenberg E, Shenkman E, Bian J. A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes. Nature Communications 2024;15(1) View
  7. Gaikwad S, Bontha M, Devi S, Dumbre D. Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques. Public Health Nursing 2025;42(1):286 View
  8. 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
  9. Camacho-Cogollo J, Patiño Zambrano C, Lochmuller C, Colmenares-Mejia C, Rozo N, Isaza-Ruget M, Rodriguez P, García A. An Application of Natural Language Processing for Hypoglycemic Event Identification in Patients with Diabetes Mellitus. Healthcare Analytics 2025:100381 View
  10. Liu Z, Chen J, Liang Q, Luo W, Cheng Q, Li Q, Mei M, Zan L. Inpatient Dietary Behaviour Score (IDBS) Predicts the Risk of Hypoglycemia in Type 2 Diabetes Mellitus (T2DM) Patients. Diabetes/Metabolism Research and Reviews 2025;41(2) View
  11. Persson V, Lovén Wickman U. Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes—An Integrative Literature Review. Healthcare 2025;13(8):950 View

Books/Policy Documents

  1. Harris Y, Reich D, Li X. Diabetes Management in Hospitalized Patients. View

Conference Proceedings

  1. Mehta J, Kalla M. 2023 World Conference on Communication & Computing (WCONF). Predicting Severity from Electronic Health Records of Leprosy Patients using Ensemble Learning View
  2. Nandanapu R, GSVV K, Reddy V. 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). Predicting Sevearity of Type 2 Diabetic Retinopathy Using Retinal Fundus Images with new DeepLearning Architectures from the Inception and Resnet Families View