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

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.