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

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

ObjectiveMetagenomic 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 interpreta...

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Bibliographic Details
Main Authors: Zhi Gao, Yongfang Jiang, Mengxuan Chen, Weihang Wang, Qiyao Liu, Jing Ma
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Cellular and Infection Microbiology
Subjects:
metagenomic next-generation sequencing (mNGS)
fever of unknown origin (FUO)
machine learning algorithms
light gradient-boosting machine (LightGBM)
predictive modeling
Online Access:https://www.frontiersin.org/articles/10.3389/fcimb.2025.1550933/full
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