Medical informatics research trend analysis: A text mining approach

Yong Mi Kim, Dursun Delen

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The objective of this research is to identify major subject areas of medical informatics and explore the time-variant changes therein. As such it can inform the field about where medical informatics research has been and where it is heading. Furthermore, by identifying subject areas, this study identifies the development trends and the boundaries of medical informatics as an academic field. To conduct the study, first we identified 26,307 articles in PubMed archives which were published in the top medical informatics journals within the timeframe of 2002 to 2013. And then, employing a text mining -based semi-automated analytic approach, we clustered major research topics by analyzing the most frequently appearing subject terms extracted from the abstracts of these articles. The results indicated that some subject areas, such as biomedical, are declining, while other research areas such as health information technology (HIT), Internet-enabled research, and electronic medical/health records (EMR/EHR), are growing. The changes within the research subject areas can largely be attributed to the increasing capabilities and use of HIT. The Internet, for example, has changed the way medical research is conducted in the health care field. While discovering new medical knowledge through clinical and biological experiments is important, the utilization of EMR/EHR enabled the researchers to discover novel medical insight buried deep inside massive data sets, and hence, data analytics research has become a common complement in the medical field, rapidly growing in popularity.

Original languageEnglish
Pages (from-to)432-452
Number of pages21
JournalHealth Informatics Journal
Volume24
Issue number4
DOIs
StatePublished - 1 Dec 2018
Externally publishedYes

Keywords

  • cluster analysis
  • electronic health records
  • medical informatics
  • PubMed
  • text mining

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