Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).

Journal: International journal of medical informatics
PMID: 39644794

Abstract

BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.

Authors

  • Hye Yoon Park
    Department of Psychiatry, Seoul National University Hospital, Seoul National University College of Medicine, South Korea; Department of Psychiatry, Seoul National University College of Medicine, South Korea.
  • Hyoju Sohn
    Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital Healthcare Innovation Park, South Korea.
  • Arum Hong
    Department of Psychiatry, Seoul National University Bundang Hospital, South Korea.
  • Soo Wan Han
    Department of Psychiatry, Seoul National University Hospital, Seoul National University College of Medicine, South Korea.
  • Yuna Jang
    Department of Psychiatry, Seoul National University Bundang Hospital, South Korea.
  • EKyong Yoon
    Department of Psychiatry, Seoul National University Bundang Hospital, South Korea.
  • Myeongju Kim
    Division of Clinical Medicine, Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam-si, Korea.
  • Hye Youn Park
    Department of Psychiatry, Seoul National University College of Medicine, South Korea; Department of Psychiatry, Seoul National University Bundang Hospital, South Korea. Electronic address: hy.park@snu.ac.kr.

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