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Open Access
Peer-reviewed
Research Article
Can machine learning improve patient selection for cardiac resynchronization therapy?
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Szu-Yeu Hu,
Roles Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Department of Radiology, Masachusetts General Hospital, Boston, Massachusetts, United States of America
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Enrico Santus,
Roles Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing
Affiliation Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America
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Alexander W. Forsyth,
Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft
Affiliation Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America
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Devvrat Malhotra,
Roles Investigation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America
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Josh Haimson,
Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing
Affiliation Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America
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Neal A. Chatterjee,
Roles Investigation, Writing – original draft, Writing – review & editing
Affiliation Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, United States of America
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Daniel B. Kramer,
Roles Conceptualization, Investigation, Writing – original draft, Writing – review & editing
Affiliation Richard A. and Susan F. Smith Center for Outcomes Research, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
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Regina Barzilay,
Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – review & editing
Affiliation Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America
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James A. Tulsky,
Roles Conceptualization, Investigation, Resources, Supervision, Writing – review & editing
Affiliations Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, Division of Palliative Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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Charlotta Lindvall
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing
* E-mail: Charlotta_lindvall@DFCI.harvard.edu
Affiliations Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, Division of Palliative Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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Can machine learning improve patient selection for cardiac resynchronization therapy?
- Szu-Yeu Hu,
- Enrico Santus,
- Alexander W. Forsyth,
- Devvrat Malhotra,
- Josh Haimson,
- Neal A. Chatterjee,
- Daniel B. Kramer,
- Regina Barzilay,
- James A. Tulsky,
- Charlotta Lindvall
- Published: October 3, 2019
- https://doi.org/10.1371/journal.pone.0222397