The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm

Background Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whet...

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Veröffentlicht in:	BMC infectious diseases Jg. 23; H. 1; S. 881 - 9
Hauptverfasser:	Chen, Lijiao, Yuan, Lingke, Sun, Tingting, Liu, Ruiqing, Huang, Qing, Deng, Shaoli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 16.12.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Active tuberculosis infection Algorithms Artificial intelligence Classification Computer-aided medical diagnosis Conductivity COVID-19 Decision trees Diagnosis Distinguish Hematology Humans Infections Infectious Diseases Internal Medicine Latent Tuberculosis - diagnosis Latent tuberculosis infection Learning algorithms Leukocyte VCS (volume, conductivity, light scatter) parameters Leukocytes (neutrophilic) Light Light scattering Lymphocytes Machine Learning Machine learning algorithm Mathematical models Medical Microbiology Medicine Medicine & Public Health Methods Monocytes Mycobacterium tuberculosis Neutrophils Pandemics Parameter identification Parasitology Performance evaluation Performance prediction Regression analysis Scattering Support vector machines Tropical Medicine Tuberculosis China United States > US Leukocyte VCS (volume, conductivity, light scatter) parameters Latent tuberculosis infection Machine learning algorithm Active tuberculosis infection Distinguish
ISSN:	1471-2334, 1471-2334
Online-Zugang:	Volltext
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