• Journal of Korean society of Dental Hygiene
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• v.21 no.5
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• pp.639-644
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• 2021

Objectives: The purpose of this study was to analyze the factors that affected the number of citations for articles published in the Journal of Korean Society of Dental Hygiene based on previous studies. Methods: Information on papers including the number of citations was collected using a web crawling technique. The effect of the number of author keywords, the number of Medical Subject Headings (MeSH) keywords, MeSH match rate, abstract word count and keyword-abstract ratio on the number of citations was analyzed by multiple regression analysis. Results: The use of the MeSH keyword did not have a significant effect on the number of citations. Among the other factors, only the keyword-abstract ratio was statistically significant. Conclusions: Select a topic of constant interest in the field, write the title in detail using colons or asterisks if necessary, and do not repeat the words used in the title in keywords. Select specific keywords deeply related to the topic. In particular, choice words or phrases that are frequently used in the abstract. If the MeSH keyword selection contradicts the previous strategies, boldly give up the MeSH keyword.

• Journal of Oriental Neuropsychiatry
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• v.23 no.4
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• pp.37-48
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• 2012

Objectives : The purpose of this study is to investigate the frequently used herbal materials among herbal prescription for vascular dementia. Methods : Every article relevant to vascular dementia was initially obtained from a Korean database and PubMed. Keywords searched were 'vascular dementia', 'herbal medicine' and 'human'. Results : Clinical study, which vascular dementia were treated with herbal medicine, were 12. Among these 12 articles, 6 were case study, 1 was Controlled Clinical Trial and 5 were Ramdomized Controlled Trial (RCT). High frequently used herbal materials were Ginseng Radix (9 times), Cnidii Rhizoma (8 times), Glycyrrhizae Radix, Citri Pericarpium, Astragali Radix and Angelicae Gigantis Radix (6 times). Conclusions : We could know frequent-used herbal medicine for vascular dementia. To be aware of the frequently used herbal medicine for vascular dementia can be helpful in adding herbal materials to prescription in a clinical treatment and development of new drugs.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.61-70
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• 2021

This study aimed to analyze the research trends of the abstract data of ergonomic studies registered in MEDLINE, a medical bibliographic database, using word embedding. Medical-related ergonomic studies mainly focus on work-related musculoskeletal disorders, and there are no studies on the analysis of words as data using natural language processing techniques, such as word embedding. In this study, the abstract data of ergonomic studies were extracted with a program written with selenium and BeutifulSoup modules using python. The word embedding of the abstract data was performed using the word2vec model, after which the data found in the abstract were vectorized. The vectorized data were visualized in two dimensions using t-Distributed Stochastic Neighbor Embedding (t-SNE). The word "ergonomics" and ten of the most frequently used words in the abstract were selected as keywords. The results revealed that the most frequently used words in the abstract of ergonomics studies include "use", "work", and "task". In addition, the t-SNE technique revealed that words, such as "workplace", "design", and "engineering," exhibited the highest relevance to ergonomics. The keywords observed in the abstract of ergonomic studies using t-SNE were classified into four groups. Ergonomics studies registered with MEDLINE have investigated the risk factors associated with workers performing an operation or task using tools, and in this study, ergonomics studies were identified by the relationship between keywords using word embedding. The results of this study will provide useful and diverse insights on future research direction on ergonomic studies.

• PNF and Movement
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• v.20 no.2
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• pp.275-283
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• 2022

Purpose: This study focused on reviewing articles published in Proprioceptive Neuromuscular Facilitation (PNF) and Movement to identify the current research trends featured in the journal. Methods: First, the most frequently used keywords in the 193 studies published in 2018 volume 16, issue 1, to 2021 volume 19, issue 3, were identified to determine the popularity of different topics. Information on the following parameters was collected for each study: research type, level of the study, research topic, diagnosis, application of PNF, and techniques applied. Results: Over the four-year period reviewed, "stroke" was the most frequently used keyword, followed by "balance" and "PNF." In terms of research type, observational analytical studies were the most frequently reported (52.85%), and experimental studies were the second-most common (37.82%). Regarding the research level, it was found that level 5 studies were the most frequent, at 49.74%, and level 2 studies accounted for 28.5% of the publications. Most of the studies stated "stroke patient" (26.42%) as the study diagnosis, except for the studies that recruited healthy people (36.79%). The majority of studies did not implement PNF treatments (15.54%), but a combination of isotonic techniques was most commonly used when PNF was applied. Conclusion: A broad range of topics and types of studies have recently been featured in the journal, including neurological impairments and musculoskeletal disorders. The findings of this review provide insight into future research trends and the direction of the journal PNF and Movement.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.91-100
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• 2021

PURPOSE: This study analyzed the trends and characteristics of shoulder rehabilitation research through keyword analysis, and their relationships were modeled using text mining techniques. METHODS: Abstract data of 10,121 articles in which abstracts were registered on the MEDLINE of PubMed with 'shoulder' and 'rehabilitation' as keywords were collected using python. By analyzing the frequency of words, 10 keywords were selected in the order of the highest frequency. Word-embedding was performed using the word2vec technique to analyze the similarity of words. In addition, the groups were classified and analyzed based on the distance (cosine similarity) through the t-SNE technique. RESULTS: The number of studies related to shoulder rehabilitation is increasing year after year, keywords most frequently used in relation to shoulder rehabilitation studies are 'patient', 'pain', and 'treatment'. The word2vec results showed that the words were highly correlated with 12 keywords from studies related to shoulder rehabilitation. Furthermore, through t-SNE, the keywords of the studies were divided into 5 groups. CONCLUSION: This study was the first study to model the keywords and their relationships that make up the abstracts of research in the MEDLINE of Pub Med related to 'shoulder' and 'rehabilitation' using text-mining techniques. The results of this study will help increase the diversifying research topics of shoulder rehabilitation studies to be conducted in the future.

• Korea Trade Review
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• v.45 no.5
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• pp.37-53
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• 2020

This study aims to provide the implications of research development by comparing domestic and international studies conducted on the subject of FTA. To this end, among the papers written during the period from 2000 to July 23, 2020, papers whose title is searched by FTA (Free Trade Agreement) were selected as research data. In the case of domestic research, 1,944 searches from the Korean Citation Index (KCI) and 970 from the Web of Science and SCOPUS were selected for international research, and the research trend was analyzed through keywords and abstracts. Frequency analysis and word embedding (Word2vec) were used to analyze the data and visualized using t-SNE and Scattertext. The results of the analysis are as follows. First, in the top 30 keywords of domestic and international research, 16 out of 30 were found to be the same. In domestic research, many studies have been conducted to analyze the outcomes or expected effects of countries that have concluded or discussed FTAs with Korea, on the other hand there are diverse range of study subjects in international research. Second, in the word embedding analysis, t-SNE was used to visually represent the research connection of the top 60 keywords. Finally, Scattertext was used to visually indicate which keywords were frequently used in studies from 2000 to 2010, and from 2011 to 2020. This study is the first to draw implications for academic development through abstract and keyword analysis by applying various text mining approaches to the FTA related research papers. Further in-depth research is needed, including collecting a variety of FTA related text data, comparing and analyzing FTA studies in different countries.

• Herbal Formula Science
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• v.31 no.1
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• pp.67-79
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• 2023

Objectives : The purpose of this study was to confirm the consumption trends of Korean medicine for health food consumption of consumers by using the Naver DataLab Shopping Insight service. Methods : In this study, the search data for the category of Korean herbal ingredients in the health food field of Naver Datalab shopping insight site was collected and sorted in order of frequency from August 1st, 2017 to June 22nd, 2022. The frequently searched keywords were organized based on the inclusion of Korean Pharmacopoeia (KP), Korean Herbal Pharmacopoeia (KHP), and Food Code. Results : 67,804 keywords were collected, and the most frequent keywords appearing for more than 200 days among the top 500 were 827 (1.184%). Among the frequent keywords, there were 149 keywords related to traditional medicine names included in the KP and KHP, and five prescriptions were included. 60 keywords were not included in the KP and KHP, and the keyword with the highest search frequency was "kujibbongnamu" (Maclura tricuspidata). Conclusions : The findings of this study provide information on the consumer's interest in traditional korean medicine (TKM) and natural products (NP), and can be used as a basis for understanding the demand for TKM and NP in the online shopping market.

• Journal of the Korean housing association
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• v.25 no.2
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• pp.79-87
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• 2014

The purpose of this study is to research the characteristics of literature related to environmentally friendly for a village-focused green index. In order to make an assessment, keywords relating to green architecture were selected: environmental friendliness, ecology, sustainable, Noksaek (Green in Korean), green, and environmentally friendly. In addition, three keywords defining the scope of space were also selected: building, village, and city. Quantitative changes and contents of articles containing the keywords were analyzed. The result is as follows. First, 'sustainable' and 'ecology' were the terms most frequently used as parts of subjects and titles, respectively. Second, the studies relating to green architecture focused on villages mostly examine the actual conditions of the villages; criteria for environmental friendliness, analyses and evaluation of the environmentally friendly features of the village; and ways to establish a green, ecological, and sustainable village. Finally, when it came to establishing a village-focused green index, the environment, resources, and energy are shown to be very important elements. In addition, the term 'ecology' in a green index is shown to be significant for the management of the natural environment.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.