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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Machine Learning (2032) Decision Support for Health Professionals (1509)

Published on in Vol 10, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37484, first published .
Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach

Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach

Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach

Authors of this article:

Caogen Hong1, 2 Author Orcid Image ;   Zhoujian Sun3 Author Orcid Image ;   Yuzhe Hao2 Author Orcid Image ;   Zhanghuiya Dong2 Author Orcid Image ;   Zhaodan Gu2 Author Orcid Image ;   Zhengxing Huang4 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. 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
  2. Yang Y, Da J, Nie S, Yuan J, Liu B, Liu H, Yang Q, Li H, Xu G, Weng J, Kong Y, Wan Q, Li G, Chen C, Xu H, Hu Y, Shi Y, Zhou Y, Su G, Tang Y, Hou F, Zha Y. A prediction model for moderate to severe acute kidney injury in people with heart failure. Military Medical Research 2024;11(1) View
  3. Cama-Olivares A, Braun C, Takeuchi T, O'Hagan E, Kaiser K, Ghazi L, Chen J, Forni L, Kane-Gill S, Ostermann M, Shickel B, Ninan J, Neyra J. Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification. Journal of the American Society of Nephrology 2025;36(10):1969 View