• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.833-836
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• 2003

본 논문에서는 생물학적 문헌으로부터 유의미한 정보를 추출하는 바이오 텍스트 마이닝의 기본 단계인 생물학적 개체명 인식 모델을 제안하였다. 기존의 생물학적 개체명 인식은 규칙 혹은 코퍼스 구축뿐만 아니라 개체명 인식에 요구되는 기본 자원을 구축하는데만도 많은 시간과 비용이 요구되므로 한정된 도메인을 대상으로 연구가 진행되어 왔다. 본 논문에서 제안하는 개체명 인식 방법은 이러한 비용 문제 및 새로운 도메인으로의 이식성 문제를 극복하기 위해 UMLS 로부터 통계적인 방법으로 정보를 추출해 기본적인 언어자원을 구축하고 이를 이용해 규칙을 생성함으로써 개체명인식을 수행한다. 본 연구에서 제안하는 방법은 바이오 텍스트 마이닝 연구의 도메인 한정적인 문제를 해결하는데 기여할 수 있을 것으로 기대된다.

• Korean Journal of Oriental Medicine
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• v.18 no.3
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• pp.133-139
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• 2012

Objective : UMLS is 'Ontology' which establishes the database for medical terminology by gathering various medical vocabularies representing same fundamental concepts. Method : Although Chinese character are represented in the Chinese part of Korean Unicode system in a computer, writing of Chinese characters is vary depending on Chinese input systems and Chinese writers' levels of knowledge. As the result of this, representation of Chinese writing in a computer will be considerably different from an old Chinese document. Therefore, a meaningful relationship between digital Chinese terminology and translated Korean is necessary in order to build Ontology for Chinese medical terms from Oriental medical prescription in a computer system. Result : This research will present 1:1 mapping information among the Chinese characters used in the Oriental medical prescription with analysis of 'same character different sound' and 'same meaning different shape' in Chinese part of Unicode systems. Conclusions : Furthermore, the research will provide top-down menu of relationship between Chinese term and Korean term in medical prescription with assumption of that the Oriental medical prescription has its own unique meaning.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.141-160
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• 2004

Recent reseach in the field of medical information systems has paid much attention to an ontology based medical terminology system to support clinical study and effective information search. This study aims to conduct research for further application or construction of ontology systems in Korea. This research reviews the theory of concept modeling and ontology. and analyses 4 cases of conceptual modeling of medical tenrminologies by ontology, The findings ot this study display these specific characteristics in medscal ontologies: (UHe standardization of terrrinology on MeSH. (2) The conceptualization of tenninology on UMLS. (l) ane. (2) are showed as untormal ontologies. (3) The theory of ontology integration in ON9, (4) The reference model of medical knowledge with formalization in GALEN. (3) and (4) are showed as formal ontologies. The application and construction of ontology should be differentiated according to the level of the proposed system, and then this analysis will provide useful information for the researcher and developer of the system.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.129-132
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• 2003

The metathesaurus(UMLS, 2003AA edition) supports multi language and includes 875, 233 concepts, 2, 146, 897 concept names. It is impossible for PubMed or NLM serve searching of the metatheaurus to retrieval using a query that is not to be text, a fault sentence structure or a part of concept name. That means the user notice correctly suitable medical words in order to get correct answer, otherwise she or he can't find information that they want to find I propose that the method of searching unified medical language system using automatic modified a query for problem that I mentioned. This method use dictionary that is standard for automation of modified query gauge similarity between query and dictionary using string comparison algorithm. And then, the tested term converse the form of metathesaurus for optimized result. For the evaluation of method, I select some query and I contrast NLM method that renewed Aug. 2003.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.155-156
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• 2015

자연어처리의 여러 분야에서 기본요소로 사용되는 영어 품사 태거를 UMLS의 의학용어 어휘정보와 OANC(Open American National Corpus) 말뭉치를 이용해 의학용 문서도 분석 가능한 의학용 영어 품사 태거를 제안한다. TRIE구조를 이용한 단어 묶음 모델로 여러 어절의 의학용어를 하나로 묶고 HMM(Hiden Markov Model)을 이용한 품사 태거로 해당하는 품사를 부착한다.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.31-34
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• 2015

본 논문에서는 임상 의사 결정 지원을 위한 UMLS와 위키피디아를 이용하여 지식 정보를 추출하고 질의 유형 정보를 이용한 LDA 기반 질의 확장 방법을 제안한다. 질의로는 해당 환자가 겪고 있는 증상들이 주어진다. UMLS와 위키피디아를 사용하여 병명과 병과 관련된 증상, 검사 방법, 치료 방법 정보를 추출한다. UMLS와 위키피디아를 사용하여 추출한 의학 정보를 이용하여 질의와 관련된 병명을 추출한다. 질의와 관련된 병명을 이용하여 추가 증상, 검사 방법, 치료 방법 정보를 확장 질의로 선택한다. 또한, LDA를 실행한 후, Word-Topic 클러스터에서 질의와 관련된 클러스터를 추출하고 Document-Topic 클러스터에서 초기 검색 결과와 관련이 높은 클러스터를 추출한다. 추출한 Word-Topic 클러스터와 Document-Topic 클러스터 중 같은 번호를 가지고 있는 클러스터를 찾는다. 그 후, Word-Topic 클러스터에서 의학 용어를 추출하여 확장 질의로 선택한다. 제안 방법의 유효성을 검증하기 위해 TREC Clinical Decision Support(CDS) 2014 테스트 컬렉션에 대해 비교 평가한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.465-465
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• 2010

A controlled medical vocabulary is a vital component of medical information management because it enables computers to use information meaningfully and different institutions to share the medical data. There are currently many standard medical vocabularies - SNOMED-CT, ICD-10, UMLS, GALEN, MED, etc, but none is universally accepted as an optimal controlled medical vocabulary for application to medical information system. Moreover, it is difficult to settle the well-designed local data dictionary consisting of controlled medical vocabularies for the individual hospital information system (HIS). One of the major reasons is the local terminology with poor contents have been used in the hospital. Thus, as a trial, the local controlled vocabulary referencing system has being constructed in a limited medical field - nuclear medicine. We selected practical nuclear medicine terms from interpretation reports and electronic medical records, and removed ambiguity and redundancy, mapping the selected terms to standard medical vocabularies. Relationship and hierarchy structure between terms have being made, referring to standard medical vocabularies. Further studies may be warranted.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.268-270
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• 2005

본 논문에서는 온톨로지를 활용하여 생물학 데이터를 효율적으로 통합 검색하기 위한 XQuery일 확장 시스템을 설계하고 구현하였다. 이를 위해 본 논문에서는 먼저 공개 생물학 온톨로지 등인 GO, UMLS들로부터 의미 있는 정보를 추출하기 위한 생물학 온톨로지 API를 온톨로지별로 정의하였다. 정의된 온톨로지 API는 본 시스템에서 사용하는 XQuery의 사용자 정의 함수로써 포함되며 이 XQuery는 본 시스템에 내장된 XQuery Expander에 의해 확장되어 처리된다. 확장된 XQuery는 온톨로지를 이용함으로써 이질적인 구조와 용어로 이루어진 생물학 데이터들을 통합 검색 할 수 있으며, 온톨로지에 정의되어 있는 지식과 관계들을 확장검색에 활용함으로써 재현율을 획기적으로 높일 수 있다. 본 논문에서는 또한 XQuery의 작성을 용이하게 할 수 있도록 지원하는 GUI 환경도 구현하였다.