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High-quality medical systematic reviews require comprehensive literaturesearches to ensure the recommendations and outcomes are sufficiently reliable.Indeed, searching for relevant medical literature is a key phase inconstructing systematic reviews and often involves domain (medical researchers)and search (information specialists) experts in developing the search queries.Queries in this context are highly complex, based on Boolean logic, includefree-text terms and index terms from standardised terminologies (e.g., theMedical Subject Headings (MeSH) thesaurus), and are difficult andtime-consuming to build. The use of MeSH terms, in particular, has been shownto improve the quality of the search results. However, identifying the correctMeSH terms to include in a query is difficult: information experts are oftenunfamiliar with the MeSH database and unsure about the appropriateness of MeSHterms for a query. Naturally, the full value of the MeSH terminology is oftennot fully exploited. This article investigates methods to suggest MeSH termsbased on an initial Boolean query that includes only free-text terms. In thiscontext, we devise lexical and pre-trained language models based methods. Thesemethods promise to automatically identify highly effective MeSH terms forinclusion in a systematic review query. Our study contributes an empiricalevaluation of several MeSH term suggestion methods. We further contribute anextensive analysis of MeSH term suggestions for each method and how thesesuggestions impact the effectiveness of Boolean queries.

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search