gms | German Medical Science

In fields that lack standardized terminology, selecting search terms for systematic reviews and meta-analyses can be a challenging and lengthy process that is susceptible to bias. Often, research teams can unintentionally exclude articles from the review by omitting synonymous phrases in their search terms. To combat these problems, we developed a quick, objective, reproducible method for generating search strategies that uses text mining and keyword co-occurrence networks to identify the most important terms for a review. The method reduces bias in search strategy development because it does not rely on a predetermined set of articles and can improve search recall by identifying synonymous terms that research teams might otherwise omit. When tested against the search strategies used in published environmental systematic reviews, our method performs as well as the published searches and retrieves gold-standard hits that replicated versions of the original searches do not. Because the method is quasi-automated, the amount of time required to develop a search strategy, conduct searches, and assemble results is reduced from approximately 17–34 hours to under 2 hours. To facilitate its use, we implemented the method in the R package litsearchr and a graphical user interface that allows users to access the core litsearchr functionality with no coding involved.

Keywords: search term selection, automated methods, keyword identification, text mining, R packages