Cargando…
A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation
BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types a...
Autores principales: | Chen, Yuanfang, Ouyang, Liu, Bao, Forrest S, Li, Qian, Han, Lei, Zhang, Hengdong, Zhu, Baoli, Ge, Yaorong, Robinson, Patrick, Xu, Ming, Liu, Jie, Chen, Shi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8030658/ https://www.ncbi.nlm.nih.gov/pubmed/33714935 http://dx.doi.org/10.2196/23948 |
Ejemplares similares
-
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach
por: Xu, Ming, et al.
Publicado: (2021) -
The bleeding phenotype in people with nonsevere hemophilia
por: Kloosterman, Fabienne R., et al.
Publicado: (2022) -
Decision Tree Model for Hematology/Oncology Patients with Severe and Nonsevere Sepsis
por: Munger, Jessica A., et al.
Publicado: (2018) -
Development and external validation of a prognostic tool for nonsevere COVID-19 inpatients
por: Luo, Ensi, et al.
Publicado: (2023) -
GENETIC RISK FACTORS IN SEVERE, NONSEVERE AND ACUTE PHENOTYPES OF CENTRAL SEROUS CHORIORETINOPATHY
por: Mohabati, Danial, et al.
Publicado: (2020)