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AI and text mining for searching and screening the literature

This guide is intended to provide an overview of the definition and application of text mining in search strategy development and study selection; it includes a list of tools and resources that librarians or other motivated searchers may wish to try

PubMed PubReMiner

To access PubMed PubReMiner:

In this example related to building a search strategy to identify studies on regulatory strategies for the prevention of vaping in youth, I am using the following records to identify words, MeSH terms, and subheadings to add to a search strategy:

33632806 33875537 33653751 32722775 33565665 33850007 33504582 33533278 33713411 33868903 33476232 33504583

  • I pulled this list of accession numbers for PubMed records (PMIDs, also called unique identifiers in Ovid MEDLINE) from relevant records identified by the research team: These records should refer to studies the researcher would consider for inclusion in a review

Based on these results, I might consider adding the subheading "legislation and jurisprudence"[subheading] in PubMed (or lj.fs. in Ovid MEDLINE) to a search strategy seeking articles on the effects of regulations on vaping in youth, for example.

For more information:

See the PubMed PubReMiner Help (FAQ)

Liaison Librarian

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Genevieve Gore
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Liaison Librarian, Schulich Library of Physical Sciences, Life Sciences, and Engineering
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