Machine learning for screening of at-risk, mild and moderate copd patients at risk of fev decline: results from copdgene and spiromics

HIGHLIGHTS

• who: April and collaborators from the Universitu00e9 Grenoble Alpes, France University of Alabama at Birmingham have published the article: Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV decline: results from COPDGene and SPIROMICS, in the Journal: (JOURNAL)
• what: The aim of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD) Global Initiative for Chronic Obstructive Lung Disease (GOLD 0) or with . . .
  

   

  

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