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http://dx.doi.org/10.15207/JKCS.2021.12.8.001

A Convergence Study of the Research Trends on Stress Urinary Incontinence using Word Embedding  

Kim, Jun-Hee (Research and developement, KOREATECH Co.)
Ahn, Sun-Hee (Research and developement, KOREATECH Co.)
Gwak, Gyeong-Tae (Coupang Co.)
Weon, Young-Soo (Department of Physical Therapy, Yonsei University)
Yoo, Hwa-Ik (Department of Physical Therapy, Yonsei University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.8, 2021 , pp. 1-11 More about this Journal
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.
Keywords
Medical data mining; Natural language processing; Convergence; t-SNE; Word2Vec;
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