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Open Access
Peer-reviewed
Research Article
Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data
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Aaron J. Masino ,
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Visualization, Writing – original draft
* E-mail: masinoa@email.chop.edu
Affiliations Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
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Mary Catherine Harris,
Roles Conceptualization, Funding acquisition, Investigation, Writing – original draft
Affiliations Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Daniel Forsyth,
Roles Software, Writing – review & editing
Affiliation Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
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Svetlana Ostapenko,
Roles Data curation, Writing – review & editing
Affiliation Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
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Lakshmi Srinivasan,
Roles Validation, Writing – review & editing
Affiliations Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Christopher P. Bonafide,
Roles Methodology, Writing – review & editing
Affiliations Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Fran Balamuth,
Roles Methodology, Writing – review & editing
Affiliations Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Melissa Schmatz,
Roles Data curation, Writing – review & editing
Affiliation Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Robert W. Grundmeier
Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Software, Writing – original draft
Affiliations Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
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Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data
- Aaron J. Masino,
- Mary Catherine Harris,
- Daniel Forsyth,
- Svetlana Ostapenko,
- Lakshmi Srinivasan,
- Christopher P. Bonafide,
- Fran Balamuth,
- Melissa Schmatz,
- Robert W. Grundmeier
- Published: February 22, 2019
- https://doi.org/10.1371/journal.pone.0212665