A question-answering framework for automated abstract screening using large language models

Akinseloyin, O., Jiang, X. orcid.org/0000-0003-4255-5445 and Palade, V. orcid.org/0000-0002-6768-8394 (2024) A question-answering framework for automated abstract screening using large language models. Journal of the American Medical Informatics Association, 31 (9). pp. 1939-1952. ISSN 1067-5027

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Item Type: Article
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© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Keywords: abstract screening; automated systematic review; large language model; question answering; zero-shot re-ranking; Natural Language Processing; Abstracting and Indexing; Systematic Reviews as Topic; Humans; Information Storage and Retrieval
Dates:
  • Published: September 2024
  • Published (online): 23 July 2024
  • Accepted: 9 July 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 18 Oct 2024 11:38
Last Modified: 18 Oct 2024 11:38
Status: Published
Publisher: Oxford University Press (OUP)
Refereed: Yes
Identification Number: 10.1093/jamia/ocae166
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Sustainable Development Goals:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
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