AKIML: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

Journal: Clinica chimica acta; international journal of clinical chemistry
PMID: 38702035

Abstract

BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days.

Authors

  • Tao Sun
    Janssen Research & Development, LLC, Raritan, NJ, USA.
  • Xiaofang Yue
    The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. Electronic address: 2611106@zju.edu.cn.
  • Gong Zhang
    College of Communication Engineering, Jilin University, Changchun 130012, China.
  • Qinyan Lin
    The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
  • Xiao Chen
  • Tiancha Huang
    The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Weiwei Liu
    School of Nursing, Capital Medical University, No. 10, Xi tou tiao, You An Men Wai, Feng tai District, Beijing, 100069 China.
  • Zhihua Tao

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