Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy.

Journal: Critical care (London, England)
PMID: 32028984

Abstract

BACKGROUND: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset.

Authors

  • Min Woo Kang
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Jayoun Kim
    Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea.
  • Dong Ki Kim
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Kook-Hwan Oh
    Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
  • Kwon Wook Joo
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Yon Su Kim
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Seung Seok Han
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.

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