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A decision support system for automating document retrieval and citation screening

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journal contribution
submitted on 2023-10-31, 06:51 and posted on 2023-10-31, 09:58 authored by Raymon van Dinter, Cagatay Catal, Bedir Tekinerdogan

The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitiveness, high human workload, and difficulty identifying all relevant literature. This study aims to reduce human workload and error of the document retrieval and primary study selection processes using a decision support system (DSS). An open-source DSS is proposed that supports the document retrieval step, dataset preprocessing, and citation classification. The DSS is domain-independent, as it has proven to carefully select an article’s relevance based solely on the title and abstract. These features can be consistently retrieved from scientific database APIs. Additionally, the DSS is designed to run in the cloud without any required programming knowledge for reviewers. A Multi-Channel CNN architecture is implemented to support the citation screening process. With the provided DSS, reviewers can fill in their search strategy and manually label only a subset of the citations. The remaining unlabeled citations are automatically classified and sorted based on probability. It was shown that for four out of five review datasets, the DSS's use achieved significant workload savings of at least 10%. The cross-validation results show that the system provides consistent results up to 88.3% of work saved during citation screening. In two cases, our model yielded a better performance over the benchmark review datasets. As such, the proposed approach can assist the development of systematic literature reviews independent of the domain. The proposed DSS is effective and can substantially decrease the document retrieval and citation screening steps' workload and error rate.

Other Information

Published in: Expert Systems with Applications
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Open Access funding provided by the Qatar National Library



  • English



Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU