Machine Learning-Based Prediction of Post-Operative Systemic Inflammatory Response Syndrome Following Pediatric Percutaneous Nephrolithotripsy.

Journal: Journal of inflammation research
Published Date: May 30, 2025

Abstract

OBJECTIVE: This study aimed to develop and validate a machine learning-based model for predicting systemic inflammatory response syndrome (SIRS) in pediatric patients undergoing percutaneous nephrolithotripsy (PCNL) and to establish a prediction platform specifically tailored for this population.

Authors

  • Nueraili Abudurexiti
    Department of Urology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, People's Republic of China.
  • Bide Liu
    Department of Urology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, People's Republic of China.
  • Shuheng Wang
    Department of Urology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, People's Republic of China.
  • Qiang Dong
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Maimaitiaili Batuer
    Department of Urology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, People's Republic of China.
  • Zewei Liu
    Faculty of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.
  • Xun Li
    Department of Laboratory Medicine, The First Affiliated Hospital of Xiamen University, Xiamen, China.

Keywords

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