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

Artificial Intelligence (1371) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (573) Machine Learning (1465) Natural Language Processing (692) Diabetes Technology Around the World (22)

Published on in Vol 21, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11990, first published .
Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

Authors of this article:

Jinying Chen1, 2 Author Orcid Image ;   John Lalor2, 3 Author Orcid Image ;   Weisong Liu2, 4 Author Orcid Image ;   Emily Druhl2 Author Orcid Image ;   Edgard Granillo1, 2 Author Orcid Image ;   Varsha G Vimalananda2, 5 Author Orcid Image ;   Hong Yu2, 3, 4, 6 Author Orcid Image
Article Authors Cited by (24) Tweetations (8) Metrics

Journals

  1. Ferrario A, Demiray B, Yordanova K, Luo M, Martin M. Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning. Journal of Medical Internet Research 2020;22(9):e19133 View
  2. López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. International Journal of Environmental Research and Public Health 2020;17(3):1093 View
  3. Hung L, Sung S, Hu Y. A Machine Learning Approach to Predicting Readmission or Mortality in Patients Hospitalized for Stroke or Transient Ischemic Attack. Applied Sciences 2020;10(18):6337 View
  4. Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors 2021;21(2):546 View
  5. 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
  6. 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
  7. JENIE R, NURDIN N, HUSEIN I, ALATAS H. Sensitivity and Specificity of Non-Invasive Blood Glucose Level Measurement Optical Device to Detect Hypoglycaemia. Journal of Nutritional Science and Vitaminology 2020;66(Supplement):S226 View
  8. 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
  9. Sung S, Hung L, Hu Y. Developing a stroke alert trigger for clinical decision support at emergency triage using machine learning. International Journal of Medical Informatics 2021;152:104505 View
  10. Davoudi A, Lee N, Luong T, Delaney T, Asch E, Chaiyachati K, Mowery D. Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study. Journal of Medical Internet Research 2022;24(6):e36151 View
  11. Chen T, Zhang Y, Dou Q, Zheng X, Wang F, Zou J, Jia R. Machine Learning-Assisted Preoperative Diagnosis of Infection Stones in Urolithiasis Patients. Journal of Endourology 2022;36(8):1091 View
  12. Yang L, Shami A. IoT data analytics in dynamic environments: From an automated machine learning perspective. Engineering Applications of Artificial Intelligence 2022;116:105366 View
  13. Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceutical Medicine 2022;36(5):295 View
  14. Sato H, Kimura Y, Ohba M, Ara Y, Wakabayashi S, Watanabe H. Prediction of Prednisolone Dose Correction Using Machine Learning. Journal of Healthcare Informatics Research 2023;7(1):84 View
  15. Zhang Y, Razbek J, Li D, Yang L, Bao L, Xia W, Mao H, Daken M, Zhang X, Cao M. Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models. BMC Public Health 2022;22(1) View
  16. Zheng Y, Dickson V, Blecker S, Ng J, Rice B, Melkus G, Shenkar L, Mortejo M, Johnson S. Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review. JMIR Diabetes 2022;7(2):e34681 View
  17. Ndichu S, Ban T, Takahashi T, Inoue D. AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods. Applied Sciences 2023;13(3):1977 View
  18. 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
  19. Mermin-Bunnell K, Zhu Y, Hornback A, Damhorst G, Walker T, Robichaux C, Mathew L, Jaquemet N, Peters K, Johnson T, Wang M, Anderson B. Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection. JAMA Network Open 2023;6(7):e2322299 View
  20. Dou M, Tang J, Tiwari P, Ding Y, Guo F. Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review. ACM Computing Surveys 2024;56(6):1 View
  21. 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
  22. Miranda E, Aryuni M, Rahmawati M, Hiererra S, Dian Sano A. Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic. Computers and Education: Artificial Intelligence 2024;7:100312 View