A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

BACKGROUND AND AIMS: The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. METHODS: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 p...

Descripción completa

Detalles Bibliográficos
Autores principales: Hong, Wandong, Zhou, Xiaoying, Jin, Shengchun, Lu, Yajing, Pan, Jingyi, Lin, Qingyi, Yang, Shaopeng, Xu, Tingting, Basharat, Zarrin, Zippi, Maddalena, Fiorino, Sirio, Tsukanov, Vladislav, Stock, Simon, Grottesi, Alfonso, Chen, Qin, Pan, Jingye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Cellular and Infection Microbiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039730/
https://www.ncbi.nlm.nih.gov/pubmed/35493729
http://dx.doi.org/10.3389/fcimb.2022.819267

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039730/
https://www.ncbi.nlm.nih.gov/pubmed/35493729
http://dx.doi.org/10.3389/fcimb.2022.819267

Ejemplares similares