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Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.
Boueiz, Adel; Xu, Zhonghui; Chang, Yale; Masoomi, Aria; Gregory, Andrew; Lutz, Sharon M; Qiao, Dandi; Crapo, James D; Dy, Jennifer G; Silverman, Edwin K; Castaldi, Peter J.
Afiliación
  • Boueiz A; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Xu Z; Pulmonary and Critical Care Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Chang Y; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Masoomi A; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States.
  • Gregory A; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States.
  • Lutz SM; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Qiao D; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States.
  • Crapo JD; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
  • Dy JG; Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, Colorado, United States.
  • Silverman EK; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States.
  • Castaldi PJ; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
Chronic Obstr Pulm Dis ; 9(3): 349-365, 2022 Jul 29.
Article en En | MEDLINE | ID: mdl-35649102
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chronic Obstr Pulm Dis Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chronic Obstr Pulm Dis Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos