Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Journal: Journal of clinical monitoring and computing
PMID: 39158783

Abstract

PURPOSE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms.

Authors

  • Heejoon Jeong
    Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
  • Donghee Kim
    Department of Artificial Intelligence, Sungkyunkwan University College of Computing and Informatics, Suwon-si, Gyeonggi, South Korea.
  • Dong Won Kim
    Department of Artificial Intelligence, Sungkyunkwan University College of Computing and Informatics, Suwon-si, Gyeonggi, South Korea.
  • Seungho Baek
    Department of Computer Science and Engineering, Sungkyunkwan University College of Computing and Informatics, Suwon-si, Gyeonggi, South Korea.
  • Hyung-Chul Lee
    From the Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.
  • Yusung Kim
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Hyun Joo Ahn

Keywords

External Resources

Popular Topics
Recent Journals