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

Machine Learning (1501) Artificial Intelligence in Diabetes Care and Prevention (29) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Secondary Use of Clinical Data for Research and Surveillance (375) Artificial Intelligence (1421)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25237, first published .
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Authors of this article:

Zakhriya Alhassan1, 2 Author Orcid Image ;   Matthew Watson1 Author Orcid Image ;   David Budgen1 Author Orcid Image ;   Riyad Alshammari3 Author Orcid Image ;   Ali Alessa4 Author Orcid Image ;   Noura Al Moubayed1 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Loh H, Ooi C, Seoni S, Barua P, Molinari F, Acharya U. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine 2022;226:107161 View
  2. Watson M, Awwad Shiekh Hasan B, Al Moubayed N. Using model explanations to guide deep learning models towards consistent explanations for EHR data. Scientific Reports 2022;12(1) View
  3. Chambers P, Watson M, Bridgewater J, Forster M, Roylance R, Burgoyne R, Masento S, Steventon L, Harmsworth King J, Duncan N, al Moubayed N. Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine 2023;12(17):17856 View
  4. Swinckels L, Bennis F, Ziesemer K, Scheerman J, Bijwaard H, de Keijzer A, Bruers J. The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review. Journal of Medical Internet Research 2024;26:e48320 View
  5. Jiang H, Wang H, Pan T, Liu Y, Jing P, Liu Y. Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors. Bioengineering 2024;11(11):1053 View
  6. Watson M, Chambers P, Steventon L, Harmsworth King J, Ercia A, Shaw H, Al Moubayed N. From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology 2024;3(1):e000430 View
  7. Watson M, Boulitsakis Logothetis S, Green D, Holland M, Chambers P, Al Moubayed N. Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics 2024;31(1):e101088 View