Cargando…
Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning
Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantita...
Autores principales: | Liu, Haipeng, Wang, Jiangtao, Geng, Yayuan, Li, Kunwei, Wu, Han, Chen, Jian, Chai, Xiangfei, Li, Shaolin, Zheng, Dingchang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518491/ https://www.ncbi.nlm.nih.gov/pubmed/36078380 http://dx.doi.org/10.3390/ijerph191710665 |
Ejemplares similares
-
Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging
por: Li, Kunwei, et al.
Publicado: (2021) -
Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores
por: Dengler, Nora Franziska, et al.
Publicado: (2021) -
Microaneurysms detection in color fundus images using machine learning based on directional local contrast
por: Long, Shengchun, et al.
Publicado: (2020) -
Representation Learning for Fine-Grained Change Detection
por: Mahony, Niall O’, et al.
Publicado: (2021) -
Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images
por: Yang, Jing, et al.
Publicado: (2022)