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

Journal: JMIR medical informatics
Published Date:

Abstract

BACKGROUND: In hospitals, Code Blue is an emergency that refers to a patient requiring immediate resuscitation. Over 85% of patients with cardiopulmonary arrest exhibit abnormal vital sign trends prior to the event. Continuous monitoring and accurate interpretation of clinical data through artificial intelligence (AI) models can contribute to preventing critical events.

Authors

  • Ji-Hyun Kim
    Researcher, HERIBio Co. Ltd., Seoul, Republic of Korea.
  • Eun Young Cho
    AITRICS Corp, 218 Teheran-ro, Gangnam-gu, Seoul, 06221, Republic of Korea, 82 025695507, 82 025695508.
  • Yuhyun Choi
    AITRICS Corp, 218 Teheran-ro, Gangnam-gu, Seoul, 06221, Republic of Korea, 82 025695507, 82 025695508.
  • Joo-Yun Won
    Medical Affairs, Aitrics Co Ltd, Seoul, Republic of Korea.
  • Se Hee Cheon
    Department of Medical Management, Presbyterian Medical Center, Jeonju, Republic of Korea.
  • Young Ae Kim
    National Cancer Control Institute, National Cancer Center, Goyang, Korea.
  • Ki-Byung Lee
    Medical Affairs, Aitrics Co Ltd, Seoul, Republic of Korea hasej@aitrics.com hochan3632@gmail.com.
  • Kwang Joon Kim
    Division of Geriatrics, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Ho Gwan Kim
    Department of Emergency Medicine, Presbyterian Medical Center, Jeonju, Republic of Korea.
  • Taeyong Sim