Identifying the landscape of Alzheimer’s disease research with network and content analysis

Min Song (), Go Eun Heo and Dahee Lee
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Min Song: Yonsei University
Go Eun Heo: Yonsei University
Dahee Lee: Yonsei University

Scientometrics, 2015, vol. 102, issue 1, No 46, 905-927

Abstract: Abstract Alzheimer’s disease (AD) is one of degenerative brain diseases, whose cause is hard to be diagnosed accurately. As the number of AD patients has increased, researchers have strived to understand the disease and develop its treatment, such as medical experiments and literature analysis. In the area of literature analysis, several traditional studies analyzed the literature at the macro level like author, journal, and institution. However, analysis of the literature both at the macro level and micro level will allow for better recognizing the AD research field. Therefore, in this study we adopt a more comprehensive approach to analyze the AD literature, which consists of productivity analysis (year, journal/proceeding, author, and Medical Subject Heading terms), network analysis (co-occurrence frequency, centrality, and community) and content analysis. To this end, we collect metadata of 96,081 articles retrieved from PubMed. We specifically perform the concept graph-based network analysis applying the five centrality measures after mapping the semantic relationship between the UMLS concepts from the AD literature. We also analyze the time-series topical trend using the Dirichlet multinomial regression topic modeling technique. The results indicate that the year 2013 is the most productive year and Journal of Alzheimer’s Disease the most productive journal. In discovery of the core biological entities and their relationships resided in the AD related PubMed literature, the relationship with glycogen storage disease is founded most frequently mentioned. In addition, we analyze 16 main topics of the AD literature and find a noticeable increasing trend in the topic of transgenic mouse.

Keywords: Alzheimer’s disease (AD); Bibliometrics; Document representation; Concept graph; Topic modeling (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11192-014-1372-x

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