MeSH Term Suggestion for Systematic Review Literature Search

Abstract

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing 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, include free-text terms and index terms from standardised terminologies (e.g., MeSH), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited.This paper investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We perform an extensive analysis of the retrieval, ranking, and refinement of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

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

“…Lexical Methods. The results of the lexical methods presented in Table 4 are the same reported in our previous work [23]. We discuss them briefly here for completeness.…”

“…The new query fragments that now contain suggested MeSH terms are then defragmented by combining all of the query fragments corresponding to the original query with the AND operator. This work extends our existing line of research into MeSH term suggestion [23], where we previously developed several techniques that depend on pre-existing lexical matching systems. One limitation of these systems is their dependence on manually crafted rules that are expensive to create and have limitations in terms of how words are matched to MeSH terms (e.g., spelling variants, acronyms, misspellings).…”

“…The following sections provide a brief overview of each of these steps. For a more comprehensive discussion of the lexical-based methods, refer to our previous work [23].…”

“…We also propose a framework to evaluate the effectiveness of the suggestion of MeSH terms on established collections of systematic review literature search queries. This article adds to a recent stream of research that has focused on computational methods for the assisted formulation [24,25,26,27] or refinement [23,28,29,30,31] of Boolean queries for systematic review creation, and more generally to research on computational methods for technology-assisted reviews [32,33,34,35,36]. Furthermore, we propose two categories of methods for the MeSH term suggestion task, including methods based on the BERT pre-trained language model and methods not based on BERT (lexical methods).…”

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

“…Our test collection will promote the development and realistic evaluation of methods that seed studies can be used to improve systematic review literature search. This includes methods we already explored in this paper like screening prioritisation [20,34] and query formulation [30], and some we leave for future works such as active learning [8] or MeSH term suggestion [33], etc. Such methods can have considerable real-world impacts, as systematic reviews are highly time consuming and costly.…”