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

Decision Support for Health Professionals (1185) Clinical Informatics (1022) Clinical Information and Decision Making (1407) Electronic Health Records (1053)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18963, first published .
Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Authors of this article:

Zakhriya Alhassan1, 2 Author Orcid Image ;   David Budgen1 Author Orcid Image ;   Riyad Alshammari3, 4 Author Orcid Image ;   Noura Al Moubayed1 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

Journals

  1. 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
  2. Luo M, Yang X. Postpartum pelvic organ prolapse assessment via adversarial feature complementation in heterogeneous data. Neural Computing and Applications 2023;35(19):13851 View