FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

Journal: Computer methods and programs in biomedicine
PMID: 37336152

Abstract

BACKGROUND AND OBJECTIVES: Intensive care unit (ICU) physicians perform weaning procedures considering complex clinical situations and weaning protocols; however, liberating critical patients from mechanical ventilation (MV) remains challenging. Therefore, this study aims to aid physicians in deciding the early liberation of patients from MV by developing an artificial intelligence model that predicts the success of spontaneous breathing trials (SBT).

Authors

  • Geun-Hyeong Kim
    Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
  • Jae-Woo Kim
    Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
  • Ka Hyun Kim
    Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
  • Hyeran Kang
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
  • Jae Young Moon
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, 35015, Rep. of Korea.
  • Yoon Mi Shin
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea. Electronic address: anees94@hanmail.net.
  • Seung Park
    Biomedical Engineering, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do 28644, Republic of Korea.

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