Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.

Journal: JMIR medical informatics
PMID: 39303280

Abstract

BACKGROUND: Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes.

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.
  • 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.
  • Shuang Gao
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Long Zhao
    Department of Respiratory Medicine and Intensive Care Unit.Peking University People's Hospital, Beijing 100044, China.
  • Chen Guan
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Xuefei Shen
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Zhihui Zhu
    Department of stomatology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China. Electronic address: 18698105576@163.com.
  • Cheng Guo
    Engineering Research Center of Automotive Electrics and Control Technology, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China. Electronic address: gchope@hnu.edu.cn.
  • Liwei Zhang
  • Chengyu Yang
    Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Quandong Bu
    Department of Nephrology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, China.
  • Bin Zhou
    Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

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