Recent Research Trends on Hypertension in Traditional Chinese Medicine (현대 중의학 관점의 고혈압 연구동향 분석)
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- The Journal of Korean Medical History
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- v.26 no.1
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- pp.107-132
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- 2013
Objectives : This study was performed to investigate the research trends of hypertension in Traditional Chinese Medicine(TCM), and it aims to promote hypertension research in Korean Medicine. Methods : We first collected more than 1,900 papers about hypertension research, and finally selected 108 papers related to this study. They were analyzed by the annul situation, the subject of the study, the perspective of TCM, the study stream, the research fields, the diagnostic patterns, and the distinguishing treatments of TCM. Results : The first review about hypertension in TCM was performed in 1963, and the number of studies has increased since 2000s. Doctors and researchers in China tried various diagnostic patterns to treat the patient because the diagnostic patterns were not unified. For this reason, most researches were reported to the diagnostic patterns and the treatments. Also we have discovered the diversity in treatments methods such as not only the typical herbal medicine, acupuncture, and Qigong but also specific treatments like Chuna, herbal acupoints stimulation, footbath, and pillow. Conclusions : Based on the results of this study, it can be proposed as follows : First, the various approach about hypertension in Korean Medicine is required. Second, the domestic research is needed to be extended to the external treatments as the distinguishing treatments of TCM.
Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (