A Data-Driven Approach for Discovering the Recent Research Status of Diabetes in China

BMC Med Inform Decis Mak

Xie

Wang

et al. 2018

Self Cite

109

BackgroundNatural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field.MethodsWe conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007–2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method.ResultsThere were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country’s publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc.ConclusionsA bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of natural language processing in medical research

BMC Med Inform Decis Mak

Xie

Wang

et al. 2018

Self Cite

109

“…Merigó et al [10] explored the research development trends in fuzzy sciences. Similar works have also been conducted in other fields, e.g., natural language processing [22], neuroimaging [23], and diabetes [24].…”

Section: Introductionmentioning

confidence: 57%

A Bibliometric Review of Natural Language Processing Empowered Mobile Computing

Wireless Communications and Mobile Computing

Ding

et al. 2018

Self Cite

Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.

“…As an effective statistical method for evaluating scientific publications, bibliometric analysis has been widely applied in various fields [18, 19]. It has been especially applied in interdisciplinary research, e.g., artificial intelligence on electronic health records research [20], natural language processing empowered mobile computing research [21], natural language processing in medical research [22], text mining in medical research [23], technology enhanced language learning research [24], and event detection in social media research [25].…”

Section: Introductionmentioning

confidence: 99%

Discovering thematic change and evolution of utilizing social media for healthcare research

BMC Med Inform Decis Mak

Lun²,

Yan³

et al. 2019

Self Cite

Background Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field. Methods With the basis of 4361 publications from both Web of Science and PubMed during the year 2008–2017, the analysis utilizes methods including topic modelling and science mapping analysis. Results Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected. Conclusions This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction. Electronic supplementary material The online version of this article (10.1186/s12911-019-0757-4) contains supplementary material, which is available to authorized users.