Literature DB >> 30376768

Medical informatics research trend analysis: A text mining approach.

Yong-Mi Kim1, Dursun Delen2.   

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.

Entities:  

Keywords:  PubMed; cluster analysis; electronic health records; medical informatics; text mining

Mesh:

Year:  2016        PMID: 30376768     DOI: 10.1177/1460458216678443

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  9 in total

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3.  Capturing the trend of mHealth research using text mining.

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5.  Characterizing Hotspots and Frontier Landscapes of Diabetes-Specific Distress from 2000 to 2018: A Bibliometric Study.

Authors:  Dan Li; Fu-Min Dai; Juan-Juan Xu; Meng-Die Jiang
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6.  Bibliometric Analysis of Renal Fibrosis in Diabetic Kidney Disease From 1985 to 2020.

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7.  The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis.

Authors:  Wenting Han; Xi Han; Sijia Zhou; Qinghua Zhu
Journal:  JMIR Med Inform       Date:  2022-01-27

8.  A Comparison of the Development of Medical Informatics in China and That in Western Countries from 2008 to 2018: A Bibliometric Analysis of Official Journal Publications.

Authors:  Jun Liang; Zhongan Zhang; Lingye Fan; Dongxia Shen; Zhenying Chen; Jie Xu; Fangmin Ge; Junyi Xin; Jianbo Lei
Journal:  J Healthc Eng       Date:  2020-10-12       Impact factor: 2.682

9.  A critical analysis of COVID-19 research literature: Text mining approach.

Authors:  Ferhat D Zengul; Ayse G Zengul; Michael J Mugavero; Nurettin Oner; Bunyamin Ozaydin; Dursun Delen; James H Willig; Kierstin C Kennedy; James Cimino
Journal:  Intell Based Med       Date:  2021-06-17
  9 in total

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