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

Artificial Intelligence (2129) Clinical Information and Decision Making (1915) Machine Learning (2076) Postoperative Monitoring and Telemonitoring (190) New and Emerging Methods and Tools in Surgery and Anesthesiology (54) Nephrology/Renal Disorders (99)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62853, first published .
A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Authors of this article:

Ji Won Min1 Author Orcid Image ;   Jae-Hong Min2 Author Orcid Image ;   Se-Hyun Chang3 Author Orcid Image ;   Byung Ha Chung4 Author Orcid Image ;   Eun Sil Koh5 Author Orcid Image ;   Young Soo Kim6 Author Orcid Image ;   Hyung Wook Kim7 Author Orcid Image ;   Tae Hyun Ban8 Author Orcid Image ;   Seok Joon Shin9 Author Orcid Image ;   In Young Choi10 Author Orcid Image ;   Hye Eun Yoon9 Author Orcid Image
Article Authors Cited by (2) Tweetations 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. Fukuyo R, Tokunaga M, Yamamoto H, Ueno H, Kinugasa Y. Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?. Annals of Gastroenterological Surgery 2025 View