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Identifying Emerging Relationship in Healthcare Domain Journals via Citation Network Analysis

Kuo-Chung Chu, Hsin-Ke Lu, Wen-I Liu

Abstract


Online e-journal databases enable scholars to search the literature in a research domain, or to cross-search an interdisciplinary field. The key literature can thereby be efficiently mapped out. This study builds a Web-based citation analysis system consisting of four modules: (1) literature search; (2) statistics; (3) articles analysis; and (4) co-citation analysis. The system focuses on the PubMed Central dataset and facilitates specific keyword searches in each research domain in terms of authors, journals, and core issues. In addition, we use data mining techniques for co-citation analysis. The results could assist researchers to develop an in-depth understanding of the research domain. An automated system for co-citation analysis promises to facilitate understanding of the changing trends that affect the journal structure of research domains. The proposed system has the potential to become a value-added database of the healthcare domain, which will benefit researchers.


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References


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DOI: https://doi.org/10.6017/ital.v37i1.9595

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