The Study on Subject Words of Korean Medical Informatics by Expanded MeSH: Based on Journal of Korean Society of Medical Informatics

Kwon, Ae Kyung; Chae, Young Moon

doi:10.4258/jksmi.2002.8.4.91

Cited by 4 publications

(3 citation statements)

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“…MeSH terms provide unity and consistency in the index as a knowledge structure [ 38 , 39 , 40 , 41 ], which distinguishes PubMed from other databases in a creative way. Accordingly, there is consistency in the index of the keywords; thus, the advantage of eliminating author subjectivity was secured when selecting keywords.…”

Section: Discussionmentioning

confidence: 99%

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Jang

2014

Healthc Inform Res

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ObjectivesUtilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65.MethodsMedical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected.ResultsHealth literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc.ConclusionsResearch on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest.

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Section: Discussionmentioning

confidence: 99%

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Jang

2014

Healthc Inform Res

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“…Papers listed in the PubMed database are reviewed by Medline and are then given MeSH-indexed terms [ 49 ], which are similar in concept to a bibliographic database. MeSH indexes and ensures consistency in medical papers [ 30 , 31 , 50 ]. Therefore, the MeSH index is more consistent and systematic than other databases.…”

Section: Discussionmentioning

confidence: 99%

“…These studies aimed to identify leading research (research front) or emerging main keywords during a particular time-period. MeSH terms are similar in concept to bibliographic database keywords [ 29 - 31 ]. The network analysis of MeSH terms excludes unstandardized keywords and prevents unwanted indexer effects.…”

Section: Introductionmentioning

confidence: 99%

Application of Social Network Analysis to Health Care Sectors

Jang

Lee

2012

Healthc Inform Res

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ObjectivesThis study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine.MethodsPapers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals.ResultsThe core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals.ConclusionsThe social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

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Keyword network analysis in stem cell research area

Jang¹

2017

J Biomed Transl Res

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The Study on Subject Words of Korean Medical Informatics by Expanded MeSH: Based on Journal of Korean Society of Medical Informatics

Cited by 4 publications

References 0 publications

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Application of Social Network Analysis to Health Care Sectors

Keyword network analysis in stem cell research area

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