Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

Liu, Rey-Long

doi:10.1371/journal.pone.0139245

“…Bibliographic coupling (BC) is a representative technique that considers out-link citations [11], which were found to be more helpful than in-link citations in the classification [12] and clustering [13] of scholarly articles. BC was also a typical citation-based similarity measure that was integrated with content-based similarity measures by hybrid measures [12][13][14][15]. The hybrid measures did not always perform significantly better than BC [12], and they even performed significantly worse on occasion [16].…”

Section: Citation-based Similarity Measuresmentioning

confidence: 99%

“…Therefore, with IBS and IIT defined for any two articles (ref. Equations (8) and (14) defined above), ICRT conducts issue-based clustering of articles by clustering the IITs (ref. Figure 2c noted above).…”

Section: Clustering Of Iits and Involving Articlesmentioning

confidence: 99%

Issue-Based Clustering of Scholarly Articles

Liu

¹

,

Hsu

²

2018

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Featured Application: The technique presented in the paper can be used to build an issue-based clustering system, which is essential for exploration and curation of research issues and their findings reported in scientific literature. Abstract:A scholarly article often discusses multiple research issues. The clustering of scholarly articles based on research issues can facilitate analyses of related articles on specific issues in scientific literature. It is a task of overlapping clustering, as an article may discuss multiple issues, and hence, be clustered into multiple clusters. Clustering is challenging, as it is difficult to identify the research issues with which to cluster the articles. In this paper, we propose the use of the titles of the references cited by the articles to tackle the challenge, based on the hypothesis that such information may indicate the research issues discussed in the article. A technique referred to as ICRT (Issue-based Clustering with Reference Titles) was thus developed. ICRT works as a post-processor for various clustering systems. In experiments on those articles that domain experts have selected to annotate research issues about specific entity associations, ICRT works with various clustering systems that employ state-of-the-art similarity measures for scholarly articles. ICRT successfully improves these systems by identifying clusters of articles with the same research focuses on specific entity associations. The contribution is of technical and practical significance to the exploration of research issues reported in scientific literature (supporting the curation of entity associations found in the literature).

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“…The next group used more advanced methods to mine various aspects of published studies. These included a wide range of goals, including predicting regulatory approval, assessing bias, extracting data from study text and figures, selecting articles for systematic review, identifying available evidence for a topic, extracting characteristics of study participants, and detecting articles that describe the same study [116][117][118][119][120][121][122][123][124][125].…”

Section: Analyzing Study Publicationsmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

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SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

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“…These included a wide range of goals, including predicting regulatory approval, assessing bias, extracting data from study text and figures, selecting articles for systematic review, identifying available evidence for a topic, extracting characteristics of study participants, and detecting articles that describe the same study [116][117][118][119][120][121][122][123][124][125].…”

Section: Analyzing Study Publicationsmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

0

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SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

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Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

Cited by 15 publications

References 36 publications

Issue-Based Clustering of Scholarly Articles

Issue-Based Clustering of Scholarly Articles

Clinical Research Informatics: Supporting the Research Study Lifecycle

Clinical Research Informatics: Supporting the Research Study Lifecycle

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