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

Clinical Informatics (1022) Electronic Health Records (1053) Machine Learning (1465) Tools, Programs and Algorithms (266) Secondary Use of Clinical Data for Research and Surveillance (366) Natural Language Processing (692) Decision Support for Health Professionals (1185)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30805, first published .
Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

Authors of this article:

Horng-Ruey Chua1, 2 Author Orcid Image ;   Kaiping Zheng3 Author Orcid Image ;   Anantharaman Vathsala1, 2 Author Orcid Image ;   Kee-Yuan Ngiam4, 5 Author Orcid Image ;   Hui-Kim Yap6, 7 Author Orcid Image ;   Liangjian Lu6, 7 Author Orcid Image ;   Ho-Yee Tiong5, 8 Author Orcid Image ;   Amartya Mukhopadhyay2, 9 Author Orcid Image ;   Graeme MacLaren5, 10 Author Orcid Image ;   Shir-Lynn Lim2, 11 Author Orcid Image ;   K Akalya1 Author Orcid Image ;   Beng-Chin Ooi3 Author Orcid Image
Article Authors Cited by (7) Tweetations (6) Metrics

Journals

  1. Tang M, Mu F, Cui C, Zhao J, Lin R, Sun K, Guan Y, Wang J. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Frontiers in Medicine 2023;9 View
  2. Ge J, Kim W, Lai J, Kwong A. “Beyond MELD” – Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. Journal of Hepatology 2022;76(6):1318 View
  3. Zheng K, Cai S, Chua H, Herschel M, Zhang M, Ooi B. DyHealth. Proceedings of the VLDB Endowment 2022;15(12):3445 View
  4. Song L, Li Y, Nie S, Feng Z, Liu Y, Ding F, Gong L, Liu L, Yang G. Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study. Clinical Cardiology 2023;46(12):1594 View
  5. Eskofier B, Klucken J. Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine. Annual Review of Biomedical Engineering 2023;25(1):131 View
  6. Bhardwaj S, Vekariya V, Singh B, Vinay S, Arul A, Roopa M. Improved healthcare monitoring of coronary heart disease patients in time-series fashion using deep learning model. Measurement: Sensors 2024;32:101053 View
  7. Heo S, Kang E, Yu J, Kim H, Lee S, Kim K, Hwangbo Y, Park R, Shin H, Ryu K, Kim C, Jung H, Chegal Y, Lee J, Park Y. Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study. JMIR Medical Informatics 2024;12:e47693 View