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

Decision Support for Health Professionals (2343) Tools, Programs and Algorithms (605) Electronic Health Records (1789) Machine Learning (3331) Cardiac Risk and Cardiac Risk Calculators (185) Artificial Intelligence (5008)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72232, first published .
Medical team attending to patient on gurney

Deep Learning–Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study

Deep Learning–Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study

Authors of this article:

Ji-hyun Kim1 Author Orcid Image ;   Eun Young Cho1 Author Orcid Image ;   Yuhyun Choi1 Author Orcid Image ;   Joo-Yun Won1 Author Orcid Image ;   Se Hee Cheon2 Author Orcid Image ;   Young Ae Kim3 Author Orcid Image ;   Ki-byung Lee1, 4 Author Orcid Image ;   Kwang Joon Kim1, 5 Author Orcid Image ;   Ho Gwan Kim6 Author Orcid Image ;   Taeyong Sim1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Puticiu M, Pop F, Butoi M, Banicioiu-Covei M, Rotaru L, Blaga T, Cimpoesu D. Artificial Intelligence in Cardiopulmonary Resuscitation. Medicina 2025;61(12):2099 View
  2. Lee G, Won J, Cho E, Kim J, Chung K, Kim K, Yun S, Lee H, Kim J. Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis. Journal of Medical Internet Research 2026;28:e93466 View
  3. Mun Y, Ahn J, Lee C, Ahn B, Huh J, Chung K. MADE: leveraging informative missingness and temporal evaluation for electronic health record-based deterioration prediction in general wards. Journal of the American Medical Informatics Association 2026 View