Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning.

Journal: European journal of medical research
Published Date:

Abstract

BACKGROUND: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine learning models to predict AKI and AKD in gout patients, with the goal of deploying web-based applications to support clinicians in making informed, real-time decisions for high-risk patients.

Authors

  • Siqi Jiang
    Department of Computer Science, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, United States.
  • Lingyu Xu
    School of Computer Engineering and Science, Shanghai University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • 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.
  • Xinyuan Wang
    Proteomics and Metabolomics Core Facilities, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Chen Guan
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, 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.
  • Xuefei Shen
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.