Your browser doesn't support javascript.
loading
Using machine learning to parse breast pathology reports.
Yala, Adam; Barzilay, Regina; Salama, Laura; Griffin, Molly; Sollender, Grace; Bardia, Aditya; Lehman, Constance; Buckley, Julliette M; Coopey, Suzanne B; Polubriaginof, Fernanda; Garber, Judy E; Smith, Barbara L; Gadd, Michele A; Specht, Michelle C; Gudewicz, Thomas M; Guidi, Anthony J; Taghian, Alphonse; Hughes, Kevin S.
Afiliación
  • Yala A; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Barzilay R; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Salama L; Department of Radiation Oncology, MGH, Boston, USA.
  • Griffin M; Division of Surgical Oncology, MGH, Boston, USA. megriff@post.harvard.edu.
  • Sollender G; Geisel School of Medicine at Dartmouth, Hanover, USA.
  • Bardia A; Department of Medical Oncology, MGH, Boston, USA.
  • Lehman C; Department of Radiology, MGH, Boston, USA.
  • Buckley JM; Division of Surgical Oncology, MGH, Boston, USA.
  • Coopey SB; Division of Surgical Oncology, MGH, Boston, USA.
  • Polubriaginof F; Department of Biomedical Informatics, Columbia University, New York, USA.
  • Garber JE; Department of Medical Oncology, DFCI, Boston, USA.
  • Smith BL; Division of Surgical Oncology, MGH, Boston, USA.
  • Gadd MA; Division of Surgical Oncology, MGH, Boston, USA.
  • Specht MC; Division of Surgical Oncology, MGH, Boston, USA.
  • Gudewicz TM; Department of Pathology, MGH, Boston, USA.
  • Guidi AJ; Department of Pathology, NWH, Newton, USA.
  • Taghian A; Department of Radiation Oncology, MGH, Boston, USA.
  • Hughes KS; Division of Surgical Oncology, MGH, Boston, USA.
Breast Cancer Res Treat ; 161(2): 203-211, 2017 01.
Article en En | MEDLINE | ID: mdl-27826755
Asunto(s)
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Registros Electrónicos de Salud / Minería de Datos / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Female / Humans Idioma: En Revista: Breast Cancer Res Treat Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Registros Electrónicos de Salud / Minería de Datos / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Female / Humans Idioma: En Revista: Breast Cancer Res Treat Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos