Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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A Convergence Study of the Research Trends on Stress Urinary Incontinence using Word Embedding

워드임베딩을 활용한 복압성 요실금 관련 연구 동향에 관한 융합 연구

  • Received : 2021.05.10
  • Accepted : 2021.08.20
  • Published : 2021.08.28
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Abstract

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

본 연구의 목적은 '복압성 요실금'을 키워드로 검색된 연구들의 경향과 특성을 단어 빈도를 통해 분석하고, 워드 임베딩을 사용하여 그 관계를 모델링 하고자 하였다. 의학 서지 데이터베이스인 MEDLINE에 등록되어 있는 복압성 요실금 연구 9,868개 논문들의 초록 문자 데이터를 Python 프로그램을 이용하여 추출하였다. 그런 다음 빈도 분석을 통해 10개의 키워드를 선택하였다. 키워드 관련 단어들의 유사도는 Word2Vec 머신러닝 알고리즘으로 분석하였다. 그리고, t-SNE 기법을 사용하여 단어의 위치와 거리가 시각화하였고, 이에 따라 그룹을 분류하여 이를 분석하였다. 복압성 요실금과 관련된 연구는 1980년대 이후 빠르게 증가했다. 키워드 분석을 통해 논문 초록에서 가장 많이 사용된 키워드는 '여성', '요도', '수술'로 나타났다. Word2Vec 모델링을 통해 복압성 요실금 관련 연구에서 주요 키워드들과 가장 높은 연관성을 나타내는 단어들에는 '여성', '절박', '증상' 등이 있었다. 그리고, t-SNE 기법을 통해 키워드와 관련 단어들은 복압성 요실금의 증상, 신체 기관의 해부학적 특성, 그리고 수술적 중재를 중심으로 하는 3개의 그룹으로 분류될 수 있었다. 본 연구는 초록을 구성하는 단어들의 키워드 빈도 분석 및 워드임베딩 방식을 이용하여 복압성 요실금 관련 연구들의 동향을 살펴본 최초의 연구이다. 본 연구의 결과는 향후 연구자들이 복압성 요실금 관련 연구 분야의 주제와 방향성을 선택하는 데 있어 기초자료로 활용될 수 있을 것이다.

Keywords

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