• 한국의학학술지인용정보
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• s.2001
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• pp.196-200
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• 2002

• 한국의학학술지인용정보
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• s.2001
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• pp.201-204
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• 2002

• 한국의학학술지인용정보
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• s.2004
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• pp.259-263
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• 2005

• 한국의학학술지인용정보
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• s.2003
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• pp.239-246
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• 2004

• 한국의학학술지인용정보
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• s.2003
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• pp.231-235
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• 2004

• 한국의학학술지인용정보
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• s.2004
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• pp.267-276
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• 2005

• Journal of Information Management
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• v.39 no.3
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• pp.49-71
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• 2008

This study aims to develop more user-oriented information literacy education for medical libraries in Korea. For this purpose, we benchmarked some advanced models in and out of Korea, and examined a case of C medical library which is medium size in Korea. Especially, we looked into the actual condition of information literacy education of C medical library and the information need of its users. We suggested some ideas to construct the programs for information literacy education of medical libraries in Korea. Especially, we proposed some considerations, contents and flow for the programs. The results of this study can be useful references whenever each medical library in Korea will start or make up for its information literacy program.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.51-55
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• 2016

본 논문에서는 임상 의사 결정 지원을 위한 UMLS와 위키피디아를 이용하여 지식 정보를 추출하고 질병중심 문서 클러스터와 단어 의미 표현을 이용하여 질의 확장 및 문서를 재순위화하는 방법을 제안한다. 질의로는 해당 환자가 겪고 있는 증상들이 주어진다. UMLS와 위키피디아를 사용하여 병명과 병과 관련된 증상, 검사 방법, 치료 방법 정보를 추출하고 의학 인과 관계를 구축한다. 또한, 위키피디아에 나타나는 의학 용어들에 대하여 단어의 효율적인 의미 추정 기법을 이용하여 질병 어휘의 의미 표현 벡터를 구축하고 임상 인과 관계를 이용하여 질병 중심 문서 클러스터를 구축한다. 추출한 의학 정보를 이용하여 질의와 관련된 병명을 추출한다. 이후 질의와 관련된 병명과 단어 의미 표현을 이용하여 확장 질의를 선택한다. 또한, 질병 중심 문서 클러스터를 이용하여 문서 재순위화를 진행한다. 제안 방법의 유효성을 검증하기 위해 TREC Clinical Decision Support(CDS) 2014, 2015 테스트 컬렉션에 대해 비교 평가한다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.167-177
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• 2013

Recently, desires for diversification of medical treatment throughout our society have been enhanced. It is thought that such a trend may be directly related to the introduction of 'alternative medicine'. This study is to establish the foundation of legalization of alternative medicine, starting with the movement for legalization of alternative medicine from constitutionality decision of medical law by the legal Information Constitutional Court. It also suggested the direction of discussion with issue of how to introduce alternative medicine beyond the stage of basic discussion, 'why' we must introduce alternative medicine, through profound investigation of preceding studies. In addition, the present study analyzed legal controversies from the appearance of alternative medicine based on the decisions of the Constitutional Court and the precedents of the Supreme Court and drew the prerequisites for the institutionalization of alternative medical treatments. It also reestablished terms of alternative medicine which have been indiscreetly used, presented methods for officialization of alternative medicine and compared and analyzed advantages and disadvantages of the methods.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.