Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.

Journal: Journal of medical Internet research
PMID: 40101226

Abstract

BACKGROUND: Most artificial intelligence-based research on acute kidney injury (AKI) prediction has focused on intensive care unit settings, limiting their generalizability to general wards. The lack of standardized AKI definitions and reliance on intensive care units further hinder the clinical applicability of these models.

Authors

  • Nam-Jun Cho
    Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea.
  • Inyong Jeong
    Department of Medical Informatics, College of Medicine, Korea University, Seoul, Korea.
  • Se-Jin Ahn
    Department of Medical Informatics, College of Medicine, Korea University, Seoul, Korea.
  • Hyo-Wook Gil
    Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea.
  • Yeongmin Kim
  • Jin-Hyun Park
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
  • Sanghee Kang
    Department of Surgery, Korea University Guro Hospital, Seoul, Republic of Korea.
  • Hwamin Lee
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.

Keywords

External Resources

Popular Topics
Recent Journals