• Journal of the Korean Society for information Management
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• v.17 no.3
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• pp.109-124
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• 2000

In order to analyze how accurately authors of Korean medical papers use MeSH terms, the key words of Korean medical papers assigned by authors (author terms) are compared with the MeSH terms listed in the corresponding MEDLINE records. A total of 1,826 author terms were used in the 415 Korean Journal of Parasitology papers published between 1989 and 1998. An average of 4.4. author terms and 9.9 MeSH terms were assigned to each paper. 35.5% of author terms matched exactly or partially with MeSH terms, the average being 1.6 terms per paper. The exact match terms consisted only 10.1%. The result of this study shows that the major difference between author terms and MeSH terms are in the use of subheadings and check tags. It indicates that the Korean authors in general do not have sufficient knowledge in selecting and using MeSH terms.

• Proceedings of the Korean Society for Information Management Conference
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• 2000.08a
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• pp.67-70
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• 2000

본 연구에서는 국내 의학학술논문의 저자가 선정한 주제용어(저자용어)와 MEDLINE 레코드의 MeSH 용어를 비교하여 국내 의학 학술논문 저자들이 얼마나 정확히 MeSH 용어를 사용하는지 일치도를 측정하였고, 사용방법상 어떠한 특징을 보이는지, 일치하지 않는 이유가 무엇인지 분석하였다. 1989년부터 1998년까지 Korean Journal of Parasitology에 발표된 415편의 논문에 사용된 1,826개의 저자용어 가운데 MEDLINE 레코드의 MeSH 용어와 일치한다고 볼 수 있는 용어는 35.5% (649개)로 한 논문에 평균 1.6개의 용어가 일치하였다. 이 가운데 완전히 일치하는 용어는 10.1%밖에 되지 않았다. 이와 같이 국내 의학학술논문 저자들은 MeSH 용어를 정확히 사용하기 위해 필수적인 체크태그 (Check tag), 계층구조 (Tree Structure), 부표목 사용 등 MeSH 용어 사용방법에 대한 지식이 부족한 것으로 나타났다.

• 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.

• Journal of Korean Ophthalmic Optics Society
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• v.21 no.2
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• pp.83-90
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• 2016

Purpose: The purpose of this study is to compare and analyze keywords of articles in the Korean Ophthalmic Optics Society to MeSH (Medical Subject Headings) terms. The study hopes to enhance the understanding and usage of MeSH and give fundamental information to the Korean Ophthalmic Optics Society in advance. Methods: A total of 1952 keywords from 409 informative articles published from 2004, Vol 9(1) to 2016, Vol 21(1) were compared with MeSH terms according to the criteria of complete coincidence, incomplete coincidence and complete incoincidence. Results: 439 keywords (22.4%) were completely coincident with MeSH terms, 815 keywords (41.8%) were incompletely coincident with MeSH terms and 693 keywords (35.5%) were completely incoincident with MeSH terms. The most used keyword in MeSH terms is in the order of Myopia, Astigmatism and visual acuity. For the incompletely coincident keywords Refractive error, Soft contact lens, and Phoria were used the most. Finally, the most used keywords in the category of completely incoincident were Accommodative lag and Pseudomonas aeruginosa. Conclusions: It is highly recommended that MeSH terms are selected as controlled keywords to increase usage of searced Korean Ophthalmic Optics Society articles in MEDLINE.

• Proceedings of the Korean Society for Information Management Conference
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• 2010.08a
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• pp.21-28
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• 2010

본 연구에서는 PubMed에 등재되었으나 Medical Subject Headings(MeSH)가 부여되지 않은 국내 의학학술지의 문헌을 대상으로 미국국립의학도서관 (NLM: National Library of Medicine)의 Medical Text Indexer(MTI)를 활용하여 MeSH 용어를 추천받은 후, PubMed 레코드의 유사주제문헌 (Relation Citations, PRC)에 부여된 MeSH와의 일치여부를 분석하였다. 또한 논문의 저자가 부여한 키워드(저자키워드)와 PRC MeSH의 일치여부도 비교하였다. PRC MeSH와 MTI MeSH 추천어의 일치율은 주표목이 21.1%였고, 체크태그는 18.1%, 부표목은 16.5%로 나타났다. 우리나라 의학논문에 나타난 저자키워드의 중요한 특징은 MeSH 주표목 위주이고, 체크태그와 부표목은 거의 사용하지 않는 것이다. 따라서 저자키워드와 PRC MeSH 주표목과의 일치율은 23.4%에 이르지만, 체크태그와 부표목의 일치율은 각각 1%, 2.1%였다. 색인전문가가 통제어휘를 사용하여 색인하는 과정에서 PRC와 MTI의 MeSH 주표목과 저자키워드가 일치하는 용어를 주표목으로 부여하고, PRC와 MTI가 추천하는 체크태그와 부표목을 활용하는 등 국내 의학문헌의 MeSH 용어 부여 작업을 반자동화(semi-indexing)하면, 정확하고 신속한 MeSH 부여 작업이 가능할 것이다.

• 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.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 Korean society of Dental Hygiene
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• v.18 no.6
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• pp.1047-1055
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• 2018

Objectives: The purpose of this study was to compare the author key words and MeSH (Medical Subject Headings) terms in the Journal of Korean Society of Dental Hygiene (JKSDH). Methods: A total of 3,242 author key words from 974 informative articles published from 2001 to 2015 were compared with MeSH terms, according to the criteria of complete coincidence, incomplete coincidence, and complete non-coincidence. Results: The coincidence rate of 564 author key words with MeSH terms was 17.4%. The author key words that appeared most frequently (in descending order) were oral health (41 times), dental hygienists (30 times), dental caries (29 times), and knowledge (29 times). There was a non-coincidence rate of 70.5% for 2,286 author key words with MeSH terms. Conclusions: Many author key words used in the JKSDH did not coincide with MeSH terms. The use of author key words that coincide with MeSH terms is necessary to accomplish the international journal.

• Journal of The Korean Dental Society of Anesthesiology
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• v.14 no.3
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• pp.143-149
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• 2014

Background: The purpose of this study was to analyze the equality between key words used in the Journal of Korean Dental Society of Anesthesiology and Medical Subject Headings (MeSH). Methods: A total of 666 English key words in 187 papers (average 3.5 words in a paper) from 2001 to 2014 were eligible for this study. We classified them according to matched, and non-matched terms. After descriptive analysis, we assayed patterns of errors in using MeSH, and reviewed frequently used non-MeSH terms. Results: Fifty nine point six percent (59.6%) of total key words were completely coincident with MeSH terms, 40.39% were not MeSH terms. Conclusions: The results show that the coincidence rate of key words with MeSH terms was at a moderate level. However, there is a need for us to understand MeSH more specifically and accurately. Use of proper key words aligned with the international standards such as MeSH is important to be properly cited. The authors should pay attention and be educated on correct use of MeSH as key words.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.82-89
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• 2004

본 논문에서는 한국어 의학 논문의 요약문으로부터 자동 영문 MeSH 키워드 제안 시스템을 소개하고, 띄어쓰기 변이(spacing variant) 문제를 해결할 수 있는 방법을 제안한다. 띄어쓰기 변이란 표준 한글 맞춤법에 비해 다르게 띄어쓰기된 것을 말한다. 이를 위해 시소러스에는 생성 가능한 모든 띄어쓰기 변이 대신에 최대 띄어쓰기 어구만을 저장하고, 문서에서 K-MeSH 용어를 찾기 위해 음절단위 부분문자열 검색을 사용한다. 이 방법으로 한국어 의학 논문의 요약문에서 K-MeSH 용어를 추출한 후, TF-IDF 순위 함수를 이용하여 상위 10위내의 키워드를 저자가 선정한 영문 키워드와 비교한 결과 58%가 일치하였다. 이는 기존 방법에 비해 42%정도의 시소러스 크기가 축소되었고, 상위 10위내에서 영문 MeSH 키워드 추천 재현률이 약 7.8% 증가한 것으로 효과적인 방법임을 보여주었다.