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Open Access
Peer-reviewed
Research Article
A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2
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Vedansh Thakkar ,
Contributed equally to this work with: Vedansh Thakkar, Greg M. Silverman
Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
* E-mail: vthakkar@umn.edu
Affiliations Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America, Natural Language Processing/Information Extraction Program, University of Minnesota, Minneapolis, Minnesota, United States of America
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Greg M. Silverman ,
Contributed equally to this work with: Vedansh Thakkar, Greg M. Silverman
Roles Formal analysis, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
Affiliations Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America, Natural Language Processing/Information Extraction Program, University of Minnesota, Minneapolis, Minnesota, United States of America
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Abhinab Kc,
Roles Formal analysis, Writing – review & editing
Affiliation University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
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Nicholas E. Ingraham,
Roles Data curation, Writing – review & editing
Affiliations Department of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota, United States of America, Center for Learning Health Systems Sciences, University of Minnesota, Minneapolis, Minnesota, United States of America
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Emma K. Jones,
Roles Data curation, Writing – review & editing
Affiliation Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America
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Samantha King,
Roles Data curation, Writing – review & editing
Affiliation Department of Surgery, University of Washington, Seattle, Washington, United States of America
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Genevieve B. Melton,
Roles Resources, Writing – review & editing
Affiliations Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America, Natural Language Processing/Information Extraction Program, University of Minnesota, Minneapolis, Minnesota, United States of America, Center for Learning Health Systems Sciences, University of Minnesota, Minneapolis, Minnesota, United States of America
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Rui Zhang,
Roles Resources, Writing – review & editing
Affiliations Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America, Natural Language Processing/Information Extraction Program, University of Minnesota, Minneapolis, Minnesota, United States of America, Center for Learning Health Systems Sciences, University of Minnesota, Minneapolis, Minnesota, United States of America
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Christopher J. Tignanelli
Roles Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing
Affiliations Department of Surgery, University of Minnesota, Minneapolis, Minnesota, United States of America, Natural Language Processing/Information Extraction Program, University of Minnesota, Minneapolis, Minnesota, United States of America, Center for Learning Health Systems Sciences, University of Minnesota, Minneapolis, Minnesota, United States of America
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A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2
- Vedansh Thakkar,
- Greg M. Silverman,
- Abhinab Kc,
- Nicholas E. Ingraham,
- Emma K. Jones,
- Samantha King,
- Genevieve B. Melton,
- Rui Zhang,
- Christopher J. Tignanelli
- Published: May 15, 2025
- https://doi.org/10.1371/journal.pone.0323535