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

Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Diabetes-specific EHR Improvements (33)

Published on in Vol 6, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10780, first published .
Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

Authors of this article:

Brian J Wells1 Author Orcid Image ;   Kristin M Lenoir1 Author Orcid Image ;   Jose-Franck Diaz-Garelli2 Author Orcid Image ;   Wendell Futrell3 Author Orcid Image ;   Elizabeth Lockerman4 Author Orcid Image ;   Kevin M Pantalone5 Author Orcid Image ;   Michael W Kattan6 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Alhassan Z, Budgen D, Alshammari R, Al Moubayed N. Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm. JMIR Medical Informatics 2020;8(7):e18963 View
  2. Oroojeni Mohammad Javad M, Agboola S, Jethwani K, Zeid A, Kamarthi S. A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study. JMIR Diabetes 2019;4(3):e12905 View
  3. Kohn M, Topaloglu U, Kirkendall E, Dharod A, Wells B, Gurcan M. Creating learning health systems and the emerging role of biomedical informatics. Learning Health Systems 2022;6(1) View
  4. Alhassan Z, Watson M, Budgen D, Alshammari R, Alessa A, Al Moubayed N. Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records. JMIR Medical Informatics 2021;9(5):e25237 View
  5. Bu L, Huang F, Li M, Peng Y, Wang H, Zhang M, Peng L, Liu L, Zhao Q. Identification of Vitamin D-related gene signature to predict colorectal cancer prognosis. PeerJ 2021;9:e11430 View
  6. Lenoir K, Sandberg J, Miller D, Wells B. Patient Perspectives on a Targeted Text Messaging Campaign to Encourage Screening for Diabetes: Qualitative Study. JMIR Formative Research 2023;7:e41011 View
  7. Liu T, Chen L, Gao G, Liang X, Peng J, Zheng M, Li J, Ye Y, Shao C, Rajakani K. Development of a Gene Risk Signature for Patients of Pancreatic Cancer. Journal of Healthcare Engineering 2022;2022:1 View
  8. Tai J, Wang L, Yan Z, Liu J. Single-cell sequencing and transcriptome analyses in the construction of a liquid–liquid phase separation-associated gene model for rheumatoid arthritis. Frontiers in Genetics 2023;14 View
  9. Yang H, Zhao A, Chen Y, Cheng T, Zhou J, Li Z. Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods. BMC Oral Health 2024;24(1) View
  10. Björneld O, Löwe W. Real-world validation of a framework for automated knowledge driven feature engineering inspired by medical domain experts. Informatics in Medicine Unlocked 2024;49:101532 View
  11. Casacchia N, Lenoir K, Rigdon J, Wells B. Development, validation and recalibration of a prediction model for prediabetes: an EHR and NHANES-based study. BMC Medical Informatics and Decision Making 2024;24(1) View

Books/Policy Documents

  1. Alhassan Z, Budgen D, Alessa A, Alshammari R, Daghstani T, Al Moubayed N. Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. View