Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

Journal: World journal of urology
PMID: 39088072

Abstract

BACKGROUND: Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case identification and prediction. However, the overall performance of AI/ML algorithms in identifying and predicting UTI has not been evaluated. The purpose of this paper is to quantitatively evaluate the application value of AI/ML in identifying and predicting UTI cases.

Authors

  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Jialu An
    Department of Information Consultation, Library of Xi'an Jiaotong University, No.76 Yan Ta West Road, Yanta District, Xi'an, 710061, China.
  • Nanding Wang
    Department of Cardiology, Xi'an Hospital of Traditional Chinese Medicine, No.69 Feng Cheng 8th Road, Weiyang District, Xi'an, 710021, China.
  • Jin Wu
    School of Information and Software Engineering, University of Electronic Science and Technology of China, China. Electronic address: wj@uestc.edu.cn.
  • Jia Yao
    Experimental Center, Xi'an Hospital of Traditional Chinese Medicine, No.69 Feng Cheng 8th Road, Weiyang District, Xi'an, 710021, China.
  • Yumei Gao
    Department of Infection Control, Xi'an Hospital of Traditional Chinese Medicine, No.69 Feng Cheng 8th Road, Weiyang District, Xi'an, 710021, China. gaoyumei_zy@163.com.

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