Your browser doesn't support javascript.
loading
Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.
Agrawal, Saaket; Klarqvist, Marcus D R; Emdin, Connor; Patel, Aniruddh P; Paranjpe, Manish D; Ellinor, Patrick T; Philippakis, Anthony; Ng, Kenney; Batra, Puneet; Khera, Amit V.
Afiliación
  • Agrawal S; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Klarqvist MDR; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building | CPZN 6.256, Boston, MA 02114, USA.
  • Emdin C; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Patel AP; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Paranjpe MD; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Ellinor PT; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building | CPZN 6.256, Boston, MA 02114, USA.
  • Philippakis A; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Ng K; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Batra P; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building | CPZN 6.256, Boston, MA 02114, USA.
  • Khera AV; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Patterns (N Y) ; 2(12): 100364, 2021 Dec 10.
Article en En | MEDLINE | ID: mdl-34950898
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Patterns (N Y) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Patterns (N Y) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos