Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

Journal: Renal failure
PMID: 39668464

Abstract

BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.

Authors

  • Lingyu Xu
    School of Computer Engineering and Science, Shanghai University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Siqi Jiang
    Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.
  • Chenyu Li
    Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi'an 710069, China.
  • Xue Gao
    Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Chen Guan
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Tianyang Li
    Sichuan University - Pittsburgh Institute (SCUPI), Sichuan University, Chengdu, 610207, China.
  • Ningxin Zhang
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Shuang Gao
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Xinyuan Wang
    Proteomics and Metabolomics Core Facilities, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yanfei Wang
    Department of Infectious Diseases, College of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
  • Lin Che
    Department of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

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