Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

Journal: BMC medical informatics and decision making
PMID: 38822285

Abstract

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques.

Authors

  • Xunliang Li
    Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Peng Wang
    Neuroengineering Laboratory, School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
  • Yuke Zhu
    Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Wenman Zhao
    Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Haifeng Pan
    Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.
  • Deguang Wang
    Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China. wangdeguang@ahmu.edu.cn.

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