Systematic review using a spiral approach with machine learning
Resource type
Journal Article
Authors/contributors
- Saeidmehr, Amirhossein (Author)
- Steel, Piers David Gareth (Author)
- Samavati, Faramarz F. (Author)
Title
Systematic review using a spiral approach with machine learning
Abstract
Abstract
With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.
Publication
Systematic Reviews
Volume
13
Issue
1
Pages
32
Date
2024-01-17
Journal Abbr
Syst Rev
Language
en
ISSN
2046-4053
Accessed
12/03/2024, 20:57
Library Catalogue
DOI.org (Crossref)
Citation
Saeidmehr, A., Steel, P. D. G., & Samavati, F. F. (2024). Systematic review using a spiral approach with machine learning. Systematic Reviews, 13(1), 32. https://doi.org/10.1186/s13643-023-02421-z
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