Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

Journal: Yonsei medical journal
PMID: 39894045

Abstract

PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using high-resolution biosignals collected within 4 h of arrival.

Authors

  • Changho Han
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Yun Jung Jung
    Department of Pulmonology and Critical Care Medicine, Ajou University School of Medicine, Suwon, Korea.
  • Ji Eun Park
    Department of Anatomy and Cell Biology, College of Medicine, Dong-A University, Busan 602-714, Korea.
  • Wou Young Chung
    Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Dukyong Yoon
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

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