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Using text mining for study identification in systematic reviews: a systematic review of current approaches
Alison O'Mara-Eves, James Thomas1, John McNaught
1Evidence for Policy and Practice Information and Coordinating (EPPI)-Centre, Social Science Research Unit, UCL Institute of Education, University of London, London, UK. j.thomas@ioe.ac.uk.
Systematic Reviews
|January 16, 2015
Summary
Text mining can help systematic reviews by automating study screening, potentially saving 30-70% of workload. Prioritizing studies with text mining is safe for live reviews, but automatic elimination requires more research.
Area of Science:
- Information Science
- Health Informatics
- Systematic Review Methodology
Background:
- Increasing publication rates complicate systematic review screening.
- Text mining offers automation to reduce reviewer workload and improve efficiency.
- This systematic review synthesizes evidence on text mining for systematic review screening.
Purpose of the Study:
- To assess the evidence base for text mining in systematic reviews.
- To evaluate workload reduction and effectiveness of semi-automation.
- To identify implementation challenges and contextual problems.
Main Methods:
- Conducted a systematic review using standard methods.
- Addressed five key research questions.
- Employed systematic searching, data extraction, and narrative synthesis.
Main Results:
- The evidence base is active but lacks collaboration and replication.
- Potential workload reductions of 30-70% are achievable.
- Screening prioritization is effective, but automatic elimination needs further validation (95% recall).
Conclusions:
- Text mining for screening prioritization is ready for use in live reviews.
- Text mining as a second screener can be used cautiously.
- Automatic study elimination shows promise but requires more development, especially outside technical/clinical fields.
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