• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.155-162
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• 2006

The medical thesaurus, MeSH (Medical Subject Heading), has been used as a controlled vocabulary thesaurus for English medical paper indexing for a long time. In this paper, we propose an automatic cross language keyword assignment method, which assigns English MeSH index terms to the abstract of a Korean medical paper. We compare the performance with the indexing performance of human indexers and the authors. The procedure of index term assignment is that first extracting Korean MeSH terms from text, changing these terms into the corresponding English MeSH terms, and calculating the importance of the terms to find the highest rank terms as the keywords. For the process, an effective method to solve spacing variants problem is proposed. Experiment showed that the method solved the spacing variant problem and reduced the thesaurus space by about 42%. And the experiment also showed that the performance of automatic keyword assignment is much less than that of human indexers but is as good as that of authors.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.383-385
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• 2003

본 논문에서는 개념 기반 검색엔진 시스템(Concept-based Search Engine System)의 검색 정확도를 향상시키기 위한 방법으로 MeSH를 이용하였다. MeSH는 Medical Subject Headings의 약자로서 MEDLINE 논문의 원활한 검색을 위하여 주제어를 코드화한 것으로 이를 개념 그래프의 시소러스로 사용하여 개념 그래프의 가장 중요한 부분인 개념 추출의 정확성을 보장하도록 하였다. 본 논문은 2003년 MeSH의 Descriptor Data의 Term 항목을 사용하여 개념과 관련이 있는 유의어를 추출했다. 추출된 유의어로 개념 그래프를 구성한 것과 문서 내에서의 단어 빈도수에 의하여 개념 그래프를 구성한 것의 검색 결과를 비교한 결과 MeSH 를 시소러스로 사용하여 개념 그래프를 구성한 것이 훨씬 더 정확한 결과를 내는 것을 확인할 수 있었다.

• Genomics & Informatics
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• v.19 no.3
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• pp.25.1-25.5
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• 2021

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. To facilitate sharing knowledge from COVID-19 papers written in English for Japanese speakers, we tried to construct a dictionary with an open license by assigning Japanese terms to MeSH unique identifiers (UIDs) annotated to words in the texts of COVID-19 papers. Using this dictionary, 98.99% of all occurrences of MeSH terms in COVID-19 papers were covered. We also created a curated version of the dictionary and uploaded it to Pub-Dictionary for wider use in the PubAnnotation system.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.79-108
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• 2021

Information retrieval in the health field has several challenges. Health information terminology is difficult for consumers (laypeople) to understand. Formulating a query with professional terms is not easy for consumers because health-related terms are more familiar to health professionals. If health terms related to a query are automatically added, it would help consumers to find relevant information. The proposed query expansion (QE) models show how to expand a query using MeSH terms. The documents were represented by MeSH terms (i.e. Bag-of-MeSH), found in the full-text articles. And then the MeSH terms were used to generate LDA (Latent Dirichlet Analysis) topic models. A query and the top k retrieved documents were used to find MeSH terms as topic words related to the query. LDA topic words were filtered by threshold values of topic probability (TP) and word probability (WP). Threshold values were effective in an LDA model with a specific number of topics to increase IR performance in terms of infAP (inferred Average Precision) and infNDCG (inferred Normalized Discounted Cumulative Gain), which are common IR metrics for large data collections with incomplete judgments. The top k words were chosen by the word score based on (TP *WP) and retrieved document ranking in an LDA model with specific thresholds. The QE model with specific thresholds for TP and WP showed improved mean infAP and infNDCG scores in an LDA model, comparing with the baseline result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.596-607
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• 2017

This study examined the method for adding related to "stomach neoplasms" as filters to the Medical Subject Headings (MeSH) for search as well as a method for improving the search efficiency through a word-proximity search by measuring the distance of co-occurring terms. A total of 8,625 articles published between 2007 and 2016 with the major topic terms "stomach neoplasms" were downloaded from PubMed article titles. The vocabulary to be added to the MeSH for search were analyzed. The search efficiency was verified by 277 articles that had "Stomach Neoplasms" indexed as MEDLINE MeSH in KoreaMed. As a result, 973 terms were selected as the candidate vocabulary. "Gastric Cancer" (2,780 appearances) was the most frequent term and 7,376 compound words (88.51%) combined the histological terms of "stomach" and "neoplasm", such as "gastric adenocarcinoma" and "gastric MALT lymphoma". A total of 5,234 compounds words (70.95%), in which the co-occurring distance was two words, were found. The matching rate through the MEDLINE MeSH and KoreaMed MeSH Indexer was 209 articles (75.5%). The search efficiency improved to 263 articles (94.9%) when the search filters were added, and to 268 articles (96.7%) when the 13 word-proximity search technique of the co-occurring terms was applied. This study showed that the use of a thesaurus as a means of improving the search efficiency in a natural language search could maintain the advantages of controlled vocabulary. The search accuracy can be improved using the word-proximity search instead of a Boolean search.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.95-115
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• 2022

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.636-645
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• 2006

A tenn with more domain specific information has higher level of term specificity. We propose new specificity calculation methods of terms based on information theoretic measures using compositional and contextual information. Specificity of terms is a kind of necessary conditions in tenn hierarchy construction task. The methods use based on compositional and contextual information of terms. The compositional information includes frequency, $tf{\cdot}idf$, bigram and internal structure of the terms. The contextual information of a tenn includes the probabilistic distribution of modifiers of terms. The proposed methods can be applied to other domains without extra procedures. Experiments showed very promising result with the precision of 82.0% when applied to the terms in MeSH thesaurus.

• Journal of the Korean Society for information Management
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• v.30 no.2
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• pp.207-225
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• 2013

Due to the development of science and technology, the convergence of various disciplines has been fostered. Accordingly, interdisciplinary studies have increasingly been expanded by integrating knowledge and methodology from different disciplines. The primary focus of biblimetric methods is on investigating the intellectual structure a field, and analysis of the characterization of interdisciplinary studies is overlooked. In this study, we aim to identify the intellectual structure of the field of medical informatics through author co-citation analysis and co-word analysis by the representative journal "IEEE ENG MED BIOL." In addition, we examine authors and MeSH Terms of top three representative journals for further analysis of the field. We examine the intellectual structure of the medical informatics field by author and word clusters to identify the network structure of medical informatics disciplines.

• Journal of Korea Technology Innovation Society
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• v.15 no.2
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• pp.369-399
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• 2012

Purpose: Health technology research & development is an important area to leading future. This study examined the current trends for 'oncogene' based on the research subject network to deduce a research front. Method: Papers were extracted from PubMed database using MeSH term for studies on 'oncogenes' and further categorized as papers published by Korean. Keywords were collected from all of articles. Research subject network was generated by keywords. Research subject network was analyzed by weighted degree centrality based social network analysis and transition of research subjects was analyzed by the time series. Results: On 'oncogenes', 'Genes, ras', 'Apoptosis', 'Signal Transduction' had a high degree centrality and currently 'Antineoplastic Agents', 'Prognosis', and 'Tumor Markers, Biological' were widely conducted. Conclusion: Consistency of research trend pattern was found by analyzing oncogene network with compromised to international vs. domestic trends. Analyzing keyword networks in various subject area, those will allow us to predict the research progress and propose evidence of research & developmental strategy.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.9-18
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• 2020

Purpose is to systematically examine the factors related to patient safety nursing of nursing university students in a convergent and complex aspect and to identify the effect size through meta-analysis. The research method used PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Medline, Embases, CINAHL, DBpia, Research Information Service System (Riss), and Korean Studies Information Service (Kiss) were used, while overseas databases were searched using MeSH terms and Emtrees. The search term was [(patient safety or patient harm or safety management) and (students, nursing)] or [(patient safety or patient harm or safety management) and (education, nursing, graduate)].The research found that nursing performance, knowledge, attitude, self-confidence, recognition, and cognition were found to be relevant factors in the order of confidence, attitude, recognition, and knowledge.