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

Clinical Information and Decision Making (1328) Machine Learning (1384) Decision Support for Health Professionals (1137) Artificial Intelligence (1197) Clinical Informatics (912)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24120, first published .
Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation

Authors of this article:

Kipyo Kim1 Author Orcid Image ;   Hyeonsik Yang2 Author Orcid Image ;   Jinyeong Yi3 Author Orcid Image ;   Hyung-Eun Son4 Author Orcid Image ;   Ji-Young Ryu4 Author Orcid Image ;   Yong Chul Kim5 Author Orcid Image ;   Jong Cheol Jeong4 Author Orcid Image ;   Ho Jun Chin4 Author Orcid Image ;   Ki Young Na4 Author Orcid Image ;   Dong-Wan Chae4 Author Orcid Image ;   Seung Seok Han5 Author Orcid Image ;   Sejoong Kim4, 6 Author Orcid Image
Article Authors Cited by (20) Tweetations (2) Metrics

Journals

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  8. Zhang X, Xue Y, Su X, Chen S, Liu K, Chen W, Liu M, Hu Y. A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(11):e38053 View
  9. Yu X, Wu R, Ji Y, Feng Z. Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide. Frontiers in Public Health 2023;11 View
  10. Naseri H, Waygood E, Wang B, Patterson Z. Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions. Transportation Research Record: Journal of the Transportation Research Board 2023;2677(12):704 View
  11. Kim M, Sohn H, Choi S, Kim S. Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare. Healthcare Informatics Research 2023;29(4):315 View
  12. Wainstein M, Flanagan E, Johnson D, Shrapnel S. Systematic review of externally validated machine learning models for predicting acute kidney injury in general hospital patients. Frontiers in Nephrology 2023;3 View
  13. Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
  14. Shi J, Bendig D, Vollmar H, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research 2023;25:e45815 View
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  16. Yang M, Liu S, Hao T, Ma C, Chen H, Li Y, Wu C, Xie J, Qiu H, Li J, Yang Y, Liu C. Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients. Artificial Intelligence in Medicine 2024;149:102785 View
  17. Zhang L, Zhang J, Gao W, Bai F, Li N, Rashid Sheykhahmad F. A novel approach for automated diagnosis of kidney stones from CT images using optimized InceptionV4 based on combined dwarf mongoose optimizer. Biomedical Signal Processing and Control 2024;94:106356 View
  18. Zappalà S, Alfieri F, Ancona A, Taccone F, Maviglia R, Cauda V, Finazzi S, Dell’Anna A. Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts. Critical Care 2024;28(1) View
  19. Naseri H, Waygood E, Patterson Z, Wang B. Who is more likely to buy electric vehicles?. Transport Policy 2024;155:15 View

Books/Policy Documents

  1. Petch J, Tabja Bortesi J, Nelson W, Di S, Mamdani M. Artificial Intelligence for Medicine. View