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A bibliometric analysis of natural language processing in medical research

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2018
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4 tweeters

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25 Dimensions

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39 Mendeley
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Title
A bibliometric analysis of natural language processing in medical research
Published in
BMC Medical Informatics and Decision Making, March 2018
DOI 10.1186/s12911-018-0594-x
Pubmed ID
Authors

Xieling Chen, Haoran Xie, Fu Lee Wang, Ziqing Liu, Juan Xu, Tianyong Hao

Abstract

Natural 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. We 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. There 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. CONCLUSIONS: A 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 23%
Student > Ph. D. Student 8 21%
Researcher 4 10%
Student > Doctoral Student 3 8%
Professor > Associate Professor 3 8%
Other 6 15%
Unknown 6 15%
Readers by discipline Count As %
Computer Science 8 21%
Medicine and Dentistry 5 13%
Business, Management and Accounting 4 10%
Biochemistry, Genetics and Molecular Biology 3 8%
Social Sciences 3 8%
Other 8 21%
Unknown 8 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 April 2018.
All research outputs
#7,347,788
of 12,739,396 outputs
Outputs from BMC Medical Informatics and Decision Making
#673
of 1,143 outputs
Outputs of similar age
#140,097
of 273,862 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 3 outputs
Altmetric has tracked 12,739,396 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,143 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 273,862 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them