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

Machine Learning (1436) Decision Support for Health Professionals (1164) Advanced Data Analytics in eHealth (125)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34724, first published .
Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Authors of this article:

Erdenebayar Urtnasan1, 2 Author Orcid Image ;   Jung Hun Lee3 Author Orcid Image ;   Byungjin Moon2 Author Orcid Image ;   Hee Young Lee2, 3 Author Orcid Image ;   Kyuhee Lee1, 2 Author Orcid Image ;   Hyun Youk2, 4 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

Journals

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  2. Stauss M, Htay H, Kooman J, Lindsay T, Woywodt A. Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?. Sensors 2023;23(3):1361 View
  3. Regolisti G, Rossi G, Genovesi S. Can we trust ECG for diagnosing hyperkalemia? A challenging question for clinicians and bioengineers. International Journal of Cardiology 2023;393:131380 View
  4. Holmstrom L, Christensen M, Yuan N, Weston Hughes J, Theurer J, Jujjavarapu M, Fatehi P, Kwan A, Sandhu R, Ebinger J, Cheng S, Zou J, Chugh S, Ouyang D. Deep learning-based electrocardiographic screening for chronic kidney disease. Communications Medicine 2023;3(1) View
  5. An J, Park M, Joo S, Chang M, Kim D, Shin D, Na Y, Kim J, Lee H, Song Y, Lee Y, Kim S. Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram. Scientific Reports 2024;14(1) View