Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography.

Journal: Scandinavian journal of trauma, resuscitation and emergency medicine
PMID: 33023615

Abstract

BACKGROUND: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG.

Authors

  • Joon-Myoung Kwon
    Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea.
  • Kyung-Hee Kim
    Department of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
  • Ki-Hyun Jeon
    Department of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
  • Soo Youn Lee
    Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, South Korea.
  • Jinsik Park
    Department of Cardiology, Mediplex Sejong Hospital, Incheon, Korea.
  • Byung-Hee Oh
    Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Republic of Korea.

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