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An automated method for developing search strategies for systematic review using Natural Language Processing (NLP)

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dc.contributor.author Kwabena, A.E.
dc.contributor.author Wiafe, O-B.
dc.contributor.author John, B-D.
dc.contributor.author Bernard, A.
dc.contributor.author Boateng, F.A.F.
dc.date.accessioned 2023-02-08T14:38:58Z
dc.date.available 2023-02-08T14:38:58Z
dc.date.issued 2023
dc.identifier.other https://doi.org/10.1016/j.mex.2022.101935
dc.identifier.uri http://ugspace.ug.edu.gh:8080/handle/123456789/38578
dc.description Research Article en_US
dc.description.abstract The design and implementation of systematic reviews and meta-analyses are often hampered by high financial costs, significant time commitment, and biases due to researchers’ familiarity with studies. We proposed and implemented a fast and standardized method for search term selection using Natural Language Processing (NLP) and co-occurrence networks to identify relevant search terms to reduce biases in conducting systematic reviews and meta-analyses. • The method was implemented using Python packaged dubbed Ananse, which is benchmarked on the search terms strategy for naïve search proposed by Grames et al. (2019) written in “R”. Ananse was applied to a case example towards finding search terms to implement a systematic literature review on cumulative effect studies on forest ecosystems. • The software automatically corrected and classified 100% of the duplicate articles identified by manual deduplication. Ananse was applied to the cumulative effects assessment case study, but it can serve as a general-purpose, open-source software system that can support extensive systematic reviews within a relatively short period with reduced biases. • Besides generating keywords, Ananse can act as middleware or a data converter for integrating multiple datasets into a database. en_US
dc.language.iso en en_US
dc.publisher MethodsX en_US
dc.subject Search Strategy en_US
dc.subject Search Terms en_US
dc.subject Data Deduplication en_US
dc.subject Software Implementation en_US
dc.subject Evidence Synthesis, en_US
dc.title An automated method for developing search strategies for systematic review using Natural Language Processing (NLP) en_US
dc.type Article en_US


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