Interpretable machine learning-derived nomogram model for early detection of persistent diarrhea in Salmonella typhimurium enteritis: a propensity score matching based case-control study.

Journal: BMC infectious diseases
PMID: 39930383

Abstract

BACKGROUND: Salmonella typhimurium infection is a considerable global health concern, particularly in children, where it often leads to persistent diarrhea. This condition can result in severe health complications including malnutrition and cognitive impairment.

Authors

  • Longteng Jin
    Department of Childhood Infectious Diseases, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China.
  • Yucheng Huang
  • Jianan Xi
    Department of Childhood Infectious Diseases, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China.
  • Lu Zhan
    School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
  • Haojie Jin
    School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
  • Yiping Chen
    Beijing Engineering Research Center for BioNanotechnology & CAS Key Laboratory for Biological Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology, Beijing 100190, PR China. Electronic address: chenyp@nanoctr.cn.
  • Maoping Chu
    Department of Pediatric Cardiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.

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