Visualising and mapping the intellectual structure of medical big data

Abstract

In this study, we sought to apply recent advances in informetrics to the analysis of literature related to big data in the field of medicine. Our aim was to elucidate research trends, identify knowledge clusters and decipher the links between them. We also sought to ascertain the theories most commonly applied in the processing of medical data and identify potential research gaps. The most important keywords over the last 10 years have been ‘big data’, ‘data mining’, ‘healthcare’, ‘cloud computing’, ‘machine learning’ and ‘electronic health record system’. These could be viewed as the core issues of research associated with big data in the field of medicine. We also identified a number of keywords that are expected to play a pivotal role in this field in the near future. These terms include the ‘internet of things’, ‘e-health’, ‘sensors’, ‘predictive modeling’, ‘quantified self’, ‘smart city’, ‘wearable device’ and ‘m-health’. Finally, we compiled co-word networks indicating the degree of connectivity between keywords, for use in locating knowledge gaps by revealing the overall context of issues commonly encountered when investigating big data. Our findings form a solid academic foundation on which to develop medical technologies, managerial strategies and theory related to big data.

Smart cities: Advances in research—An information systems perspective

“…Applications such as smart healthcare and analytics can use big data for health monitoring using IoT technologies such as sensors and smart wearable devices. The associated life signs monitoring aspect of smart health care in the context of big data within smart cities is a factor is discussed in Hsu and Li (2018), where the study highlights the year on year increases in advancement of this technology and importance for citizens.…”

Sustainability and Tourism Competitiveness in Protected Areas: State of Art and Future Lines of Research

“…To study the lines of research and groups of topics addressed in depth, a co-word analysis was conducted. The co-word analysis studies the conceptual structure of a research field through the most important words and keywords of the documents [106,108], with the purpose of identifying knowledge groups, trends of knowledge, and where to expand in the future [109,110]. This analysis is based on the premise that keywords reflect the research hotspots in a discipline field [105,107] and that they are adequate to define the content of a paper [111].…”

“…The representation of topics in a strategic diagram confirms the low maturity of the subject (Figure 10). The strategic diagram was made with the SPSS statistical software based on the data generated by CiteSpace and this approach has been widely used in co-word analysis to determine the importance of the topics and their level of development, thus allowing for the detection of topics that although they are important, are still underdeveloped (see for example [108][109][110]113,114]).…”

“…Sustainability 2019, 11, x FOR PEER REVIEW 18 of 32 subject ( Figure 10). The strategic diagram was made with the SPSS statistical software based on the data generated by CiteSpace and this approach has been widely used in co-word analysis to determine the importance of the topics and their level of development, thus allowing for the detection of topics that although they are important, are still underdeveloped (see for example, [108][109][110]113,114]). When establishing the quadrants based on the averages of centrality and density [113], "national park" and "sustainability" are the most clearly located terms in the area of central and developed topics.…”

Mapping of diseases from clinical medicine research—a visualization study

“…Meanwhile, medical scholars had adopted the bibliometric method to find theme trends and knowledge structures in different medical fields. For instance, Zhang visualized knowledge domain of patient adherence by co-word analysis and social network analysis (Zhang et al 2012); Fu utilized bibliometric analysis to research malaria in China during 2004 to 2014 (Fu et al 2015); Gu, Hsu, and Liao all visualized the big data research in medicine (Gu et al 2017;Hsu and Li 2019;Liao et al 2018); Huang analyzed the pelvic organ prolapse during 2007 to 2016 by bibliometric and social network analysis (Huang et al 2018); Jin and Xin visualized the hotspots and trends of multimedia big data (Jin and Li 2018); Zhao used co-word analysis to study the theme trends and knowledge structure on choroidal neovascularization (Zhao et al 2018); Saheb adopted bibliometric analysis to exploring IoT big data analytics in the healthcare industry (Saheb and Izadi 2019); Wang applied co-word analysis to investigate the professional-patient relations in the Internet era (Wang et al 2019); and Yang used the same method to identify the trends in research on Vitamin D (Yang et al 2019).…”