Prediction of the development of acute kidney injury following cardiac surgery by machine learning.

Journal: Critical care (London, England)
Published Date: Jul 31, 2020

Abstract

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI.

Authors

  • Po-Yu Tseng
    Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, No. 155, Section 2, Li-Nong Street, Beitou District, Taipei, 11221, Taiwan.
  • Yi-Ting Chen
    Muen Biomedical and Optoelectronics Technologies Inc., New Taipei City, Taiwan.
  • Chuen-Heng Wang
    Muen Biomedical and Optoelectronics Technologies Inc., New Taipei City, Taiwan.
  • Kuan-Ming Chiu
    Division of Cardiovascular Surgery, Cardiovascular Center, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Yu-Sen Peng
    Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Shih-Ping Hsu
    Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Kang-Lung Chen
    Division of Cardiovascular Surgery, Cardiovascular Center, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
  • Chih-Yu Yang
    Division of Nephrology in Taipei Veterans General Hospital, Taipei 11217, Taiwan.
  • Oscar Kuang-Sheng Lee
    Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Orthopedics, China Medical University Hospital, Taichung, Taiwan. Electronic address: oscarlee9203@gmail.com.

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