Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23508, first published .
Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study

Journals

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  2. Huang H, Yu S, Zheng J, Tian L. The Clinical Nursing Pathway on Prevention of Catheter Slippage with Intensive Care Unit Patients: A Systematic Review and Meta-Analysis. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
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  4. Perry-Eaddy M, Braccialarghe K, Cowl A, Melendez E. Can an unplanned extubation checklist solely identify children at-risk for adverse events? A response to the pediatric unplanned extubation risk score. Heart & Lung 2023;62:278 View
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  6. Ma H, Pan H, Dong X, Li L. An Empirical Study of Feedforward Control in Unplanned Extubation of Nasogastric Tube. Journal of Multidisciplinary Healthcare 2023;Volume 16:1465 View
  7. Zhang J, Ma G, Peng S, Hou J, Xu R, Luo L, Hu J, Yao N, Wang J, Huang X. Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study. Journal of Medical Internet Research 2023;25:e49016 View
  8. Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts’ consensus on the application of intensive care big data. Frontiers in Medicine 2024;10 View
  9. Xu X, Shen L, Qu Y, Li D, Zhao X, Wei H, Yue S. Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification. Scientific Reports 2024;14(1) View
  10. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Perspectivas actuales sobre el uso de la inteligencia artificial en la seguridad del paciente crítico. Medicina Intensiva 2025;49(3):154 View
  11. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Current perspectives on the use of artificial intelligence in critical patient safety. Medicina Intensiva (English Edition) 2025;49(3):154 View
  12. Wu J, Xiao Z, Chen S, Huang B, Han S, Huang H. Development of an evidence‐based nursing practice program for preventing unplanned endotracheal extubations in the intensive care unit: A Delphi method study. Journal of Clinical Nursing 2025;34(3):990 View
  13. Varón-Vega F, Tuta-Quintero E, Maldonado-Franco A, Robayo-Amórtegui H, Giraldo-Cadavid L, Botero-Rosas D. Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial. The Journal of Critical Care Medicine 2025;11(1):70 View
  14. Yang H, Hao A, Liu S, Chang Y, Tsai Y, Weng S, Chan M, Wang C, Xu Y. Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study. JMIR Medical Informatics 2025;13:e64592 View
  15. Zhou L, Geng K, Yu C. Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis. Journal of Multidisciplinary Healthcare 2025;Volume 18:2799 View
  16. Mou H, Ergashev A, Zhou B, Ye N, Li X. Risk Prediction of Unplanned Extubation in Inpatients Using Random Forest and Logistic Regression Models. Journal of Patient Safety 2025 View
  17. Chen Z, Wu H, Yao Z, Liu Q, Zhang H, Li X, Yao L, Yang X. Using ML techniques to predict extubation outcomes for patients with central nervous system injuries in the Yun-Gui Plateau. Scientific Reports 2025;15(1) View
  18. Kim Y, Kim M, Kim Y, Choi M. Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review. International Journal of Nursing Studies 2025:105133 View