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

Decision Support for Health Professionals (1127) Data Science (223) Electronic Health Records (961) Machine Learning (1370) Natural Language Processing (636) Diabetes-specific EHR Improvements (29)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14340, first published .
Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

Authors of this article:

Yonghao Jin1 Author Orcid Image ;   Fei Li1 Author Orcid Image ;   Varsha G Vimalananda2, 3 Author Orcid Image ;   Hong Yu1, 2, 4, 5 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

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  1. Jang R, Kim N, Jang M, Lee K, Lee S, Lee K, Noh H, Seo J. Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers. JMIR Medical Informatics 2020;8(8):e18089 View
  2. Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors 2021;21(2):546 View
  3. Pilla S, Park J, Schwartz J, Albert M, Ephraim P, Boulware L, Mathioudakis N, Maruthur N, Beach M, Greer R. Hypoglycemia Communication in Primary Care Visits for Patients with Diabetes. Journal of General Internal Medicine 2021;36(6):1533 View
  4. Kodama S, Fujihara K, Shiozaki H, Horikawa C, Yamada M, Sato T, Yaguchi Y, Yamamoto M, Kitazawa M, Iwanaga M, Matsubayashi Y, Sone H. Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis. JMIR Diabetes 2021;6(1):e22458 View
  5. Turchin A, Florez Builes L. Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review. Journal of Diabetes Science and Technology 2021;15(3):553 View
  6. Bright R, Rankin S, Dowdy K, Blok S, Bright S, Palmer L. Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method. JMIRx Med 2021;2(3):e27017 View
  7. Yang H, Li J, Liu S, Yang X, Liu J. Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation. JMIR Medical Informatics 2022;10(6):e36958 View
  8. Aljamaan I, Al-Naib I. Prediction of Blood Glucose Level Using Nonlinear System Identification Approach. IEEE Access 2022;10:1936 View
  9. Feng Z, Wu X, Ma J, Li M, He G, Cao D, Yang G. DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications. Briefings in Bioinformatics 2023;24(4) View
  10. Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Medical Informatics 2023;11:e47833 View
  11. Rehman N, Contreras I, Beneyto A, Vehi J. The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis. Mathematics 2024;12(10):1567 View