Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

Journal: BMC urology
Published Date: Jul 8, 2024

Abstract

OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).

Authors

  • Tianwei Zhang
  • Ling Zhu
    Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China. Electronic address: jjzhuling@163.com.
  • Xinning Wang
    Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xiaofei Zhang
  • Zijie Wang
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.
  • Shang Xu
    Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Wei Jiao
    Department of Spinal Surgery, Fuyang City People's Hospital, Fuyang Anhui, 236000, P.R.China.

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