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

Artificial Intelligence (1186) Machine Learning (1370) Clinical Information and Decision Making (1314) Clinical Informatics (904) Decision Support for Health Professionals (1127)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41142, first published .
Machine Learning–Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study

Machine Learning–Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study

Machine Learning–Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study

Authors of this article:

Xiao-Qin Luo1 Author Orcid Image ;   Yi-Xin Kang1 Author Orcid Image ;   Shao-Bin Duan1 Author Orcid Image ;   Ping Yan1 Author Orcid Image ;   Guo-Bao Song2 Author Orcid Image ;   Ning-Ya Zhang3 Author Orcid Image ;   Shi-Kun Yang4 Author Orcid Image ;   Jing-Xin Li5 Author Orcid Image ;   Hui Zhang6 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. Nagy M, Onder A, Rosen D, Mullett C, Morca A, Baloglu O. Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning. Pediatric Nephrology 2024;39(4):1263 View
  2. Dong Z, Liang X. A glimpse of recent advances in the research of acute kidney injury. Medicine Advances 2023;1(2):158 View
  3. Wang L, Duan S, Yan P, Luo X, Zhang N. Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care. Renal Failure 2023;45(1) View
  4. Liu F, Yao J, Liu C, Shou S. Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis. BMC Surgery 2023;23(1) View
  5. Yang X, Qiu H, Wang L, Wang X. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e44417 View
  6. Jia T, Xu K, Bai Y, Lv M, Shan L, Li W, Zhang X, Li Z, Wang Z, Zhao X, Li M, Zhang Y. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study. BMC Medical Informatics and Decision Making 2023;23(1) View
  7. Gao Y, Wang C, Dong W, Li B, Wang J, Li J, Tian Y, Liu J, Wang Y. An Explainable Machine Learning Model to Predict Acute Kidney Injury After Cardiac Surgery: A Retrospective Cohort Study. Clinical Epidemiology 2023;Volume 15:1145 View
  8. Nedadur R, Bhatt N, Chung J, Chu M, Ouzounian M, Wang B. Machine learning and decision making in aortic arch repair. The Journal of Thoracic and Cardiovascular Surgery 2023 View
  9. Shi S, Xiong C, Bie D, Li Y, Wang J. Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery. Pediatric Cardiology 2024 View
  10. Zhang W, Chang Y, Cheng C, Zhao X, Tang X, Lu F, Hu Y, Yang C, Ding Y, Shi R. A machine learning model for predicting acute kidney injury secondary to severe acute pancreatitis. Chinese Medical Journal 2024;137(5):619 View
  11. Chang T, Chen Y, Lu H, Wu J, Mak K, Yu C. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine 2024;103(7):e37112 View
  12. Lu X, Chen Y, Zhang G, Zeng X, Lai L, Qu C. Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU. Journal of Stroke and Cerebrovascular Diseases 2024;33(7):107729 View