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Open Access
Peer-reviewed
Research Article
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
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Chenxi Huang,
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America
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Karthik Murugiah,
Roles Methodology, Writing – original draft, Writing – review & editing
Affiliation Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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Shiwani Mahajan,
Roles Investigation, Writing – original draft, Writing – review & editing
Affiliation Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America
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Shu-Xia Li,
Roles Formal analysis, Methodology, Validation, Writing – review & editing
Affiliation Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America
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Sanket S. Dhruva,
Roles Methodology, Writing – review & editing
Affiliations Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
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Julian S. Haimovich,
Roles Methodology, Software, Writing – review & editing
Affiliation Albert Einstein College of Medicine, Bronx, New York, United States of America
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Yongfei Wang,
Roles Data curation, Methodology, Writing – review & editing
Affiliation Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America
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Wade L. Schulz,
Roles Methodology, Writing – review & editing
Affiliations Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America, Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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Jeffrey M. Testani,
Roles Methodology, Writing – review & editing
Affiliation Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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Francis P. Wilson,
Roles Methodology, Writing – review & editing
Affiliation Section of Nephrology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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Carlos I. Mena,
Roles Methodology, Writing – review & editing
Affiliation Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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Frederick A. Masoudi,
Roles Investigation, Methodology, Resources, Writing – review & editing
Affiliation Division of Cardiology, School of Medicine, University of Colorado, Aurora, Colorado, United States of America
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John S. Rumsfeld,
Roles Investigation, Methodology, Resources, Writing – review & editing
Affiliation Division of Cardiology, School of Medicine, University of Colorado, Aurora, Colorado, United States of America
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John A. Spertus,
Roles Methodology, Writing – review & editing
Affiliation Department of Cardiology, Saint Luke’s Mid America Heart Institute, Kansas City, Missouri, United States of America
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Bobak J. Mortazavi ,
Roles Conceptualization, Data curation, Methodology, Software, Writing – review & editing
¶‡ These authors are joint senior authors on this work.
Affiliation Department of Computer Science & Engineering, Texas A&M University, College Station, Texas, United States of America
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Harlan M. Krumholz
Roles Conceptualization, Methodology, Supervision, Writing – review & editing
* E-mail: harlan.krumholz@yale.edu
¶‡ These authors are joint senior authors on this work.
Affiliations Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America, Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America
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Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
- Chenxi Huang,
- Karthik Murugiah,
- Shiwani Mahajan,
- Shu-Xia Li,
- Sanket S. Dhruva,
- Julian S. Haimovich,
- Yongfei Wang,
- Wade L. Schulz,
- Jeffrey M. Testani,
- Francis P. Wilson
- Published: November 27, 2018
- https://doi.org/10.1371/journal.pmed.1002703