• Journal of Information Management
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• v.32 no.2
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• pp.26-39
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• 2001

Various controlled vocabularies such as thesaurus and classification used for effective information retrieval contain different terms in expressing the same concept or meaning. National Library of Medicine has developed the Unified Medical Language System(UMLS) to solve the problems of information retrieval and integration resulted from the difference of concepts between different sources. The UMLS development was initiated in 1982 as a long-term project, and the 2001 edition of the UMLS consists of three parts : Metathesaurus, Semantic Network, and SPECIALIST Lexicon. This paper reviews background and structure of the UMLS including applications in PubMed, NLM Gateway.

• 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.

• Journal of the Korean Institute of Oriental Medical Informatics
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• v.16 no.1
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• pp.1-8
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• 2010

Objective: Various controlled vocabulary such as thesaurus and classification make us to reuse and share effectively by defining different concept and linking terms each other. The UMLS(Unified Medical Language System) is one of the most universal medical terminology systems. It is needed various methods to share and reuse information of traditional Korean medicine. We will research on method that adopt SUI of the UMLS(that is de facto standard in medical terminology system) in traditional Korean medical terminology. Method: We described major problems and applying process when we tried to add traditional Korean medicine in the part of meridian into the UMLS metathesaurus. Comparing western medical terms and traditional Korean medical terms for applying UMLS metathesaurus, there is not only many consistency, but also differences. Result: We confirmed what is the differences and consistency between western medical terms and traditional Korean medical terms. And then reviewed methods that apply the CUI, LUI, SUI in traditional Korean medical terms. Traditional Korean medical terms are not discriminated by singular or plural string. In addition, traditional Korean medical terms have vary string by initial law: the law of initial sound of a syllable. Character is described with Korean, traditional Chinese, modern Chinese, etc. According to meaning, language, initial law, SUI has a distinct value respectively. Conclusion: There are many differences to apply the UMLS between western medical terms and traditional Korean medical terms. For the better implementation to traditional Korean medicine into the UMLS, further research is needed in standardization and classification of traditional Korean medical terms, medical information system, etc. We hope this study helps the implementation UMLS, EHR, knowledge based system in Oriental medicine in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.50-52
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• 2009

Ontology maintenance is a time consuming and costly process which requires special skill and knowledge. It requires joint effort of both ontology engineer and domain specialist to properly maintain ontology and update knowledge in it. This is specially true for medical domain which is highly specialized domain. This paper proposes a novel approach for maintenance and update of existing ontologies in a medical domain. The proposed approach is based on modified Ontology Maturing Process which was originally developed for web domain. The proposed approach provides way to populate medical ontology with new knowledge obtained from medical documents. This is achieved through use of natural language processing techniques and highly specialized medical knowledge bases such as Unified Medical Language 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 Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Journal of Korean Medical classics
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• v.23 no.6
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• pp.15-26
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• 2010

Objective This is a previous study for including traditional Korean medical terms into the UMLS(The Unified Medical Language System) and achieving the interoperability between various medical systems. Method First, the traditional medical terms were divided into 4 categories : basic theory, acupuncture, herb and formulae. And then, searching these terms through metathesaurus in UMLSKS(UMLS Knowledge Source Server), terminology information was investigated and analyzed. Results In the case of TM title, traditional Korean medicine was categorized as different semantic type from Traditional medicine, Traditional Chinese medicine and Kampo medicine. Acupuncture points were described only as abbreviation and herbs were conceptualized inconsistently, as some belonged to scientific name and some belonged to Chinese pronunciation. Formulaes are described as Chinese, Japanese and Korean pronunciations. Conclusions More research is needed on diagnosis/disease terms and semantic types for the unique concepts in traditional Korean medicine in order to including the international standard.

• Journal of Korean Medical classics
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• v.22 no.1
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• pp.191-205
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• 2009

Objective : Up to the present, theories of medical books is too difficult to understand thoroughly. However, these study methods have some problems in dealing with lots of meaning because the comprehension of theories are dependent upon one's memory. Especially, comparison distinct medical books are more difficult matter. So, we have attempted to solve a problem. Method : We have researched medical terms in the "Piweilun" according to below the procedure. (1) Making a terms list: We have selected constituent of sentence. And we have made term list on the basis of concept of term. (2) Making a synonym list: We have collected identical conception and made a synonym list. So, using an synonym tables of DB, it is possible to search for the non-standard terms of medical theory. (3) Making a classification system: Using UMLS(Unified Medical Language System), MeSH(Medical Subject Headings), IST(International Standard Terminology) ect., we have made a classification system of oriental medicine terms in the "Piwelun". Analysis of relation between terms. Result : In the "Piweilun", there are more than 1,790s concepts. Parts of those are belonged to UMLS-Semantic Type, the other parts of those are not belonged to UMLS-Semantic Type. And those include predicate more than UMLS-Semantic Relations.

• Journal of Korean Medical classics
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• v.23 no.1
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• pp.59-79
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• 2010

Objective : We did a comparative study about characteristics of oriental medical books. As a result, we took notice of classification in the nominal terms by semantic type of UMLS(Unified Medical Language System). By using classified nominal terms, comparative study can be more effectively. So, we selected another oriental medical book and classified nominal terms in it by semantic type of UMLS. By result of classification, we have attempted to study about comparison between oriental medical books and development of medical theories. Method :We have made a comparative study on classification in the nominal terms between "Biwiron(脾胃論)" and "Soayakjeungjikgyeol(小兒藥證直訣)" according to the below the procedure. (1) Making a nominal terms list of "Soayakjeungjikgyeol(小兒藥證直訣)" and grasping contextual meaning of nominal terms of it. (2) Modification and supplementation about semantic type of UMLS for "Soayakjeungjikgyeol(小兒藥證直訣)". Using the modified classification system, we classified nominal terms. After this process, we arranged classified nominal terms by Haansoft Hangul 2007. (3) Comparing classified nominal terms between "Biwiron(脾胃論)" and "Soayakjeungjikgyeol(小兒藥證直訣)". Result : In the "Soayakjeungjikgyeol(小兒藥證直訣)", there are more than 2,519's nominal terms and different categories of semantic type of UMLS classification from "Biwiron(脾胃論)". Through comparison between their classification of nominal terms, we can understand the characteristics of the two and their development of medical theories.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.