title: Examining the Distribution, Modularity, and Community Structure in Article Networks for Systematic Reviews.
abstract: Systematic reviews (SRs) provide high quality evidence for clinical practice, but the article screening process is time and labor intensive. As SRs aim to identify relevant articles with a specific scope, we propose that a pre-defined article relationship, using similarity metrics, could accelerate this process. In this study, we established the article relationship using MEDLINE element similarities and visualized the article network with the Force Atlas layout. We also analyzed the article networks with graph diameter, closeness centrality, and module classes. The results revealed the distribution of articles and found that included articles tended to aggregate together in some module classes, providing further evidence of the existence of strong relationships among included articles. This approach can be utilized to facilitate the articles selection process through early identification of these dominant module classes. We are optimistic that the use of article network visualization can help better SR work prioritization.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH; Department of Computer Science and Engineering, The Ohio State University, Columbus, OH.
authors.forename: Xiaonan
authors.initials: X
authors.lastname: Ji
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH.
authors.forename: Raghu
authors.initials: R
authors.lastname: Machiraju
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH.
authors.forename: Alan
authors.initials: A
authors.lastname: Ritter
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH.
authors.forename: Po-Yin
authors.initials: PY
authors.lastname: Yen
mesh_terms: D006801:Humans; D016239:MEDLINE; D016571:Neural Networks, Computer; D012196:Review Literature as Topic; D013223:Statistics as Topic