Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.

Journal: BMC medical informatics and decision making
PMID: 40211277

Abstract

BACKGROUND: The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL.

Authors

  • Haofang Zhang
    School of Data Science and Technology, Heilongjiang University, Harbin 150000, China.
  • Changbao Xu
    Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Chenge Hu
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.
  • Yunlai Xue
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.
  • Daoke Yao
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.
  • Yifan Hu
    Tencent You Tu Lab, Tencent, Shenzhen, China.
  • Ankang Wu
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.
  • Miao Dai
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.
  • Hang Ye
    The Second Clinical Medical School of Zhengzhou University, No. 2 Jingba Road, Jinshui District, Zhengzhou, 450000, China.

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