Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Trend Analysis of Thyroid Cancer Research in Korea with Text Mining Techniques

  • Lee, Tae-Gyeong (Dept. of Applied Mathematics, Kumoh National Institute of Technology) ;
  • Heo, Seong-Min (Dept. of Applied Mathematics, Kumoh National Institute of Technology) ;
  • Shin, Seung-Hyeok (Dept. of Applied Mathematics, Kumoh National Institute of Technology) ;
  • Yang, Ji-Yeon (Dept. of Applied Mathematics, Kumoh National Institute of Technology)
  • Received : 2018.10.18
  • Accepted : 2018.11.10
  • Published : 2018.12.31
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Abstract

In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

Keywords

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Fig. 1. The number of publications in domestic journals over time

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Fig. 4. Dendrogram from hierarchical clustering of keywords with cluster annotations

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Fig. 2. Word clouds of keywords by decade

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Fig. 3. (a) Complete and (b) simple social networks of keywords with cluster annotations

Table 1. The number of publications and the average length of abstracts by discipline of the journals

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Table 2. The top correlated words with specific main terminologies

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Table 3. Frequency of appearances of statistical related terms in abstracts

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