Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics.

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成果类型：

期刊论文

作者：

Zhang, Tian;Deng, Ying;Wang, Wentao;Zhao, Zhe;Wu, Yiling;...

作者机构：

[Deng, Ying; Wang, Haoqian; Xia, Shutao; Zhang, Tian] Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China

[Wu, Yiling; Xia, Shutao; Zhang, Tian] Peng Cheng Laboratory, Shenzhen, China

[Wang, Wentao; Zhao, Zhe] Zhengzhou KingMed Center for Clinical Laboratory, Zhengzhou, Henan, People's Republic of China

[Liao, Weifang] College of life science and technology, Wuhan Polytechnic University, Wuhan, People's Republic of China. leesalwf89@126.com

[Liao, Weijie] Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China. liaoweijie@szu.edu.cn

语种：

英文

关键词：

Machine learning models;Metatranscriptomic sequencing;Patients in severe condition;Prompt diagnosis;Pulmonary infections

期刊：

Scientific Reports

ISSN：

2045-2322

年：

2025

卷：

期：

页码：

30516

DOI：

10.1038/s41598-025-15914-9

机构署名：

本校为其他机构

院系归属：

生命科学与技术学院

摘要：

The prompt diagnosis of pulmonary infections with unknown etiology in patients in severe condition remains a challenge due to the lack of rapid and effective diagnostic methods. While metatranscriptomic sequencing offers a powerful approach, its clinical utility is often limited by issues of timeliness. In this study, we conducted metatranscriptomic sequencing on bronchoalveolar lavage fluid (BALF) collected from critically ill, severely ill, and ICU patients. Based on microbial detection results, patients were classified into four types: negat...

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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics.

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