Text Network Analysis to Develop a Search Strategy for a Systematic Review

• Published in: Applied sciences Vol. 14; no. 19; p. 8909
• Main Authors: Leem, Subeen, Shin, Jieun, Kim, Jong-Yeup, Shim, Sung Ryul
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Arsenic; Bibliometrics; Cancer; centrality; Data collection; Decision making; Disease; Drinking water; Evidence-based medicine; Heavy metals; Keywords; Medical research; Medical Subject Headings-MeSH; PICO; Research methodology; search strategy; Software; Systematic review; text network analysis; Canada; South Korea
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14198909

Setting the population, intervention, comparison, and outcome (PICO) elements during a search strategy development stage for a systematic review (SR) defines a research question specifically. In contrast to traditional methods that rely on researcher discretion, we propose a text network analysis (TNA) method using the R language to set the correct basis for the PICO. First, we collected 80 related papers from the PubMed database using ‘Health Impact Assessment of arsenic exposure’ as an example topic. Next, we recorded the keywords of each paper into a dataframe and converted the dataframe into an edge list format to create a network. Finally, we confirmed the connectivity and frequency of each keyword through network visualization and the importance of keywords according to three metrics through centrality analysis. As a result, arsenic could be expected to have detrimental effects on the occurrence of heart- and blood-related diseases or on mothers. By setting important keywords as the PICO elements known through a TNA, the reliability of SRs is improved, and this methodology can be equally applied to various topics.