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

Clinical Information and Decision Making (1916) Nephrology/Renal Disorders (99) Machine Learning (2077) Clinical Informatics (1326)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66568, first published .
Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study

Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study

Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study

Authors of this article:

Nam-Jun Cho1 Author Orcid Image ;   Inyong Jeong2 Author Orcid Image ;   Se-Jin Ahn2 Author Orcid Image ;   Hyo-Wook Gil1 Author Orcid Image ;   Yeongmin Kim2 Author Orcid Image ;   Jin-Hyun Park2 Author Orcid Image ;   Sanghee Kang3 Author Orcid Image ;   Hwamin Lee2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Li Y, Xiao M, Li Y, Lv L, Zhang S, Liu Y, Zhang J. Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation. JMIR Medical Informatics 2025;13:e72349 View
  2. Jiang S, Xu L, Li C, Wang X, Guan C, Wang Y, Che L, Shen X, Xu Y. Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning. European Journal of Medical Research 2025;30(1) View
  3. Shickel B, Ozrazgat-Baslanti T, Bihorac A. Artificial intelligence at the bedside for the prevention, detection, and management of acute kidney injury. Current Opinion in Nephrology & Hypertension 2025;34(6):483 View
  4. Ye J, Huang J. Opinions and attitudes toward artificial intelligence among operating room nurses: a descriptive meta-analysis based on the comparative studies of the different opinions. Frontiers in Artificial Intelligence 2025;8 View