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Examining the Distribution, Modularity, and Community Structure in Article Networks for Systematic Reviews
Xiaonan Ji1, Raghu Machiraju2, Alan Ritter2
1Department of Biomedical Informatics, The Ohio State University, Columbus, OH; Department of Computer Science and Engineering, The Ohio State University, Columbus, OH.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 10, 2016
Summary
This study introduces article network visualization to speed up systematic reviews (SRs). By analyzing article similarities, we can identify key groups of relevant papers, improving SR efficiency.
Area of Science:
- Bibliometrics
- Information Science
- Medical Informatics
Background:
- Systematic reviews (SRs) are crucial for clinical practice but involve time-consuming article screening.
- Efficiently identifying relevant literature is a major challenge in SRs.
Purpose of the Study:
- To propose and evaluate a novel method for accelerating the article screening process in SRs.
- To leverage article similarity metrics and network visualization for improved SR efficiency.
Main Methods:
- Established article relationships using MEDLINE element similarities.
- Visualized article networks using the Force Atlas layout.
- Analyzed article networks with graph diameter, closeness centrality, and module classes.
Main Results:
- Article networks revealed distinct clusters, with included articles aggregating in specific module classes.
- Identified strong relationships among relevant articles within these clusters.
- Demonstrated the potential for early identification of dominant article groups.
Conclusions:
- Article network visualization, based on similarity metrics, can significantly facilitate the article selection process in SRs.
- This approach aids in prioritizing SR work by identifying key article clusters.
- The findings support the integration of network analysis into SR methodologies for enhanced efficiency.
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