A Visual Analysis Approach to Update Systematic Reviews

TL;DR

This paper introduces USR-VTM, a visual text mining approach and tool that enhances the process of updating systematic reviews by efficiently selecting new evidence, outperforming manual methods in accuracy.

Contribution

It presents a novel visual text mining method and a supporting tool, Revis, to improve the efficiency and accuracy of updating systematic reviews.

Findings

Increases correct inclusion of new studies

Outperforms traditional manual updating methods

Supports more timely systematic review updates

Abstract

Context: In order to preserve the value of Systematic Reviews (SRs), they should be frequently updated considering new evidence that has been produced since the completion of the previous version of the reviews. However, the update of an SR is a time consuming, manual task. Thus, many SRs have not been updated as they should be and, therefore, they are currently outdated. Objective: The main contribution of this paper is to support the update of SRs. Method: We propose USR-VTM, an approach based on Visual Text Mining (VTM) techniques, to support selection of new evidence in the form of primary studies. We then present a tool, named Revis, which supports our approach. Finally, we evaluate our approach through a comparison of outcomes achieved using USR-VTM versus the traditional (manual) approach. Results: Our results show that USR-VTM increases the number of studies correctly included compared to the traditional approach. Conclusions: USR-VTM effectively supports the update of SRs.