Abstract
Due to the increasing popularity of new research in medicine thisstudy was conducted to determine recent research trends of predictive, preventive and personalized medicine (PPM). We identified the terms relevant to PPM using own search engine based on neural network processing in PubMed database. We extracted initially about 15000 articles. Then we carried out the statistical analysis for identifying research trends. The article presents the results of solving the problem of evaluating research topics at the level of thematic clusters in a separate subject area. An approach based on the analysis of article titles has been implemented. Identification of terms, connections between them and thematic clustering were carried out using the free software VOSViewer, which allows to extract terms in the form of noun phrases, as well as to cluster them.
Highlights
Bibliometric mapping helps transform most of the metadata of publications into maps or visualizations, from which useful information can be obtained through post-processing.Bibliometry is one of statistical methods to analyze the mass of literature and to reveal historical development [3], as well as a scientific qualitative and quantitative study of publications
Many authors used bibliometric in different areas of medicine, such as ophthalmology [4], rheumatology [5], otolaryngology [6], nephrology [7], geriatrics [8], etc
The PubMed database created by the National Center for Biotechnology Information (NCBI) in the United States was identified as the first base for collecting information
Summary
Bibliometric mapping helps transform most of the metadata of publications into maps or visualizations, from which useful information can be obtained through post-processing.Bibliometry is one of statistical methods to analyze the mass of literature and to reveal historical development [3], as well as a scientific qualitative and quantitative study of publications. Zhu & Guan [9] looked at keywords and topic categories of publications as actors for mapping a keyword sharing network and a topic sharing network and compared them with corresponding random binary networks. Most of these studies focus on identifying major trends in the form of the most cited studies or the most frequently used terms. While this is an excellent method for identifying major research topics [9], emerging or potentially interesting topics may not be easy to spot
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