Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity.

Journal: Frontiers in cellular and infection microbiology
PMID: 40302920

Abstract

OBJECTIVE: Metagenomic next-generation sequencing (mNGS) can potentially detect various pathogenic microorganisms without bias to improve the diagnostic rate of fever of unknown origin (FUO), but there are no effective methods to predict mNGS-positive results. This study aimed to develop an interpretable machine learning algorithm for the effective prediction of mNGS results in patients with FUO.

Authors

  • Zhi Gao
    Department of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yongfang Jiang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Mengxuan Chen
    Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Weihang Wang
    School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
  • Qiyao Liu
    Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Jing Ma
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.

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