Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV(1) decline: results from COPDGene and SPIROMICS

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV(1)) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung...

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Detalles Bibliográficos
Autores principales: Wang, Jennifer M., Labaki, Wassim W., Murray, Susan, Martinez, Fernando J., Curtis, Jeffrey L., Hoffman, Eric A., Ram, Sundaresh, Bell, Alexander J., Galban, Craig J., Han, MeiLan K., Hatt, Charles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Physiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161244/
https://www.ncbi.nlm.nih.gov/pubmed/37153221
http://dx.doi.org/10.3389/fphys.2023.1144192

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161244/
https://www.ncbi.nlm.nih.gov/pubmed/37153221
http://dx.doi.org/10.3389/fphys.2023.1144192

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