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Open Access
Peer-reviewed
Research Article
A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data
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Ravi B. Parikh,
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft
Affiliations Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America, VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America, Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Kristin A. Linn,
Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing
Affiliation Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Jiali Yan,
Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – review & editing
Affiliations Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Matthew L. Maciejewski,
Roles Methodology, Writing – review & editing
Affiliation Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, North Carolina, United States of America
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Ann-Marie Rosland,
Roles Writing – review & editing
Affiliation VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America
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Kevin G. Volpp,
Roles Writing – review & editing
Affiliations Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America, VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America, Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Peter W. Groeneveld,
Roles Writing – review & editing
Affiliations VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Amol S. Navathe
Roles Conceptualization, Formal analysis, Methodology, Supervision, Visualization, Writing – review & editing
* E-mail: amol@pennmedicine.upenn.edu
Affiliations Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America, VA Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States of America, Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data
- Ravi B. Parikh,
- Kristin A. Linn,
- Jiali Yan,
- Matthew L. Maciejewski,
- Ann-Marie Rosland,
- Kevin G. Volpp,
- Peter W. Groeneveld,
- Amol S. Navathe
- Published: February 19, 2021
- https://doi.org/10.1371/journal.pone.0247203