Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.

Journal: Journal of medical Internet research
PMID: 40173433

Abstract

BACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs.

Authors

  • Chanmin Park
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Changho Han
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea.
  • Su Kyeong Jang
    BUD.on Inc., Seoul, Republic of Korea.
  • Hyungjun Kim
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Sora Kim
    Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea.
  • Byung Hee Kang
    Department of Surgery, Division of Trauma Surgery, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Kyoungwon Jung
    Department of Surgery, Division of Trauma Surgery, 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.

Keywords

External Resources

Popular Topics
Recent Journals