Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
By Iain James Marshall, Byron C. Wallace. Systematic Reviews, 2019
491 citations recorded by the source corpus.
Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7bbc1c1351981075379c34bc4fd16f922c48201c
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In Artificial Intelligence
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In Statistics and Probability
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Citations in the corpus
- MSˆ2: Multi-Document Summarization of Medical Studies (2021, methodology): “For a detailed analysis of automated approaches in aiding the systematic review process, see Norman (2020); Marshall and Wallace (2019).”
- Fact or Fiction: Verifying Scientific Claims (2020, methodology): “Automated evidence synthesis (Marshall and Wallace, 2019; Beller et al., 2018; Tsafnat et al., 2014; Marshall et al., 2017) seeks to automate the process of creating systematic reviews of the medical literature7 – for instance, by extracting PICO snippets (Nye et al.”
- Automation of systematic literature reviews: A systematic literature review (2021, background): “Marshall and Wallace [28] list tools that are useful for systematic reviews.”