Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients.

Journal: Journal of infection and public health
PMID: 37988812

Abstract

BACKGROUND: Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. Several models have been proposed to predict urine culture positivity based on urinalysis. However, most of them have not been externally validated or consisted solely of urinalysis data obtained using one specific commercial analyzer.

Authors

  • Min Hyuk Choi
    Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Korea.
  • Dokyun Kim
    Department of Laboratory Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, South Korea; Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: kyunsky@yuhs.ac.
  • Yongjung Park
    Department of Laboratory Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, South Korea. Electronic address: YPARK119@yuhs.ac.
  • Seok Hoon Jeong
    Department of Laboratory Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, South Korea; Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, South Korea.

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