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Can machine learning improve patient selection for cardiac resynchronization therapy?
Hu, Szu-Yeu; Santus, Enrico; Forsyth, Alexander W; Malhotra, Devvrat; Haimson, Josh; Chatterjee, Neal A; Kramer, Daniel B; Barzilay, Regina; Tulsky, James A; Lindvall, Charlotta.
Afiliación
  • Hu SY; Department of Radiology, Masachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Santus E; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Forsyth AW; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Malhotra D; Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America.
  • Haimson J; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Chatterjee NA; Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, United States of America.
  • Kramer DB; Richard A. and Susan F. Smith Center for Outcomes Research, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
  • Barzilay R; Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Tulsky JA; Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
  • Lindvall C; Division of Palliative Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
PLoS One ; 14(10): e0222397, 2019.
Article en En | MEDLINE | ID: mdl-31581234
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Selección de Paciente / Terapia de Resincronización Cardíaca / Aprendizaje Automático Tipo de estudio: Guideline / Prognostic_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Selección de Paciente / Terapia de Resincronización Cardíaca / Aprendizaje Automático Tipo de estudio: Guideline / Prognostic_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos