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Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections
Joint Authors
Liang, Huiying
Zheng, Lingling
Lin, Fangqin
Zhu, Changxi
Liu, Guangjian
Wu, Xiaohui
Wu, Zhiyuan
Zheng, Jianbin
Xia, Huimin
Cai, Yi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-27
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis.
Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection.
We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis.
Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital.
Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients.
The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission.
Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94±0.054 and 0.80±0.085, respectively.
Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure.
The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.
American Psychological Association (APA)
Zheng, Lingling& Lin, Fangqin& Zhu, Changxi& Liu, Guangjian& Wu, Xiaohui& Wu, Zhiyuan…[et al.]. 2020. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
Modern Language Association (MLA)
Zheng, Lingling…[et al.]. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
American Medical Association (AMA)
Zheng, Lingling& Lin, Fangqin& Zhu, Changxi& Liu, Guangjian& Wu, Xiaohui& Wu, Zhiyuan…[et al.]. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136359