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Data mining versus manual screening to select papers for inclusion in systematic reviews: a novel method to increase
Elena Ierardi1, J Chris Eilbeck2, Frederike van Wijck3
1Department of Occupational Therapy, and Human Nutrition and Dietetics, School of Health and Life Sciences, Glasgow Caledonian University, Glasgow.
Summary
A new Python algorithm significantly speeds up systematic reviews by using computer-aided keyword identification to screen abstracts, saving 73% of the time compared to manual methods.
Area of Science:
- Medical Informatics
- Systematic Review Methodology
- Computational Biology
Background:
- Systematic reviews are crucial for evidence-based medicine but are time- and resource-intensive.
- Manual abstract screening is a bottleneck in the systematic review process.
- Efficient identification of relevant studies is essential for timely knowledge synthesis.
Purpose of the Study:
- To develop and evaluate a computer-aided algorithm for keyword identification to streamline abstract screening in systematic reviews.
- To compare the accuracy and efficiency of the algorithm-based method against traditional manual screening.
- To assess the algorithm's applicability and time-saving potential in different systematic review contexts.
Main Methods:
- A Python algorithm was developed to identify keywords within abstracts for systematic reviews.
- The algorithm's performance was evaluated for accuracy in keyword identification and abstract eligibility assessment.
- Time taken for screening using the algorithm was compared to manual screening in an exemplar systematic review of arm impairment after stroke.
- The algorithm was externally validated on a second, different systematic review.
Main Results:
- The algorithm demonstrated high accuracy, failing on only 2.6% of documents in the exemplar review.
- Both the algorithm and manual methods identified the same 610 studies for inclusion.
- Computer-aided screening reduced screening time per abstract by 73% (1.15 minutes saved per abstract).
- External validation confirmed the algorithm's effectiveness in identifying eligible studies for a different review.
Conclusions:
- The purpose-built software provides an accurate and significantly time-saving method for identifying eligible abstracts for systematic reviews.
- This novel computational approach can be adapted for various systematic reviews, benefiting researchers, reviewers, and editors.
- The algorithm offers a scalable and efficient solution to accelerate evidence synthesis and knowledge dissemination.
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