• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.323-325
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• 2014

SNOMED CT is a standard clinical terminology which is to efficiently record, manage and utilize clinical records during clinical processes. However, huge expressive power of SNOMED CT makes it difficult to select appropriate terms during short consultation hours. In addition, true meaning of a written record using the terminology may be misunderstood or even distorted since one clinical meaning could be expressed in a variety of ways. This paper analyzes such known problems in a database point of view, and presents effective selection methods of similar terminologies to mitigate the problem.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.364-372
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• 2008

It is highly useful in an actual clinical setting to apply appropriate medical terms to every area of electronic medical record (EMR) and link them effectively, as a single medical terminology system cannot cover all medical concepts. In order to use standardized terms conveniently and efficiently, it is required to categorize them depending on the purpose of individual departments or physicians and thereby develop organized subsets of extracted terms highly likely to be used. In addition, it is important to such a subset to make it possible to change or correct standardized terminology system and continue to develop and upgrade to meet renewed demands of users. In this paper, data including chief compliant, symptoms, diagnosis, operation, and history of previous treatments were collected from discharge summary of patients with Department of Neurosurgery at Busan National University Hospital for analysis. In addition, subset database was created, and for terms needed to be added, the physician directly performed mapping through connection with reference terminology server and developed subset editor for the purpose of creating new subset database. Therefore, it is expected that this can serve as a practical and effective management method to reduce problems and inefficiency caused by existing vast terminology 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.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.1
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• pp.1-8
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• 2023

The purpose of this study is to find terms that can give identity to the major and occupation of clinical laboratory technologist (also known as medical technologist). The term clinical pathology includes all branches of pathology, namely anatomical pathology, chemical pathology, hematology, microbiology, and all respective subspecialties. Unfortunately, several countries exclude anatomical pathology from the term clinical pathology, a problem that gets compounded when the title is translated into languages other than English. Clinical pathology (US, UK) is a medical specialty. Similar terms are laboratory medicine (Germany, Poland), medical/clinical biology (France, Netherlands) or clinical analysis (Spain). Depending on the person questioned, medical technology is defined slightly differently by individuals, companies, and institutions. The definition also depends on the language in which the question is asked. Medical technology can be translated to define clinical laboratory technology, allied health sciences, medical equipment, biomedical engineering, and health technology. The terms 'clinical pathology technology and pathological technology' are not used in allied health sciences. The names of 'medical technology·medical technologist' can be replaced by 'biomedical laboratory science·biomedical laboratory technologist' or 'clinical laboratory analysis·clinical laboratory analyst'. In this study, it is proposed to change the name of academic and occupation to 'medical biology·medical biology technologist' that combines the term biomedical.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.2
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• pp.85-97
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• 2024

목적 : 본 연구는 한의약에서 사용하는 용어가 SNOMED CT로 매핑 가능한지 여부를 조사하고, 한의약 용어를 표현하기 위해 기존 SNOMED CT 온톨로지를 개선할 수 있는 방안을 제안하는 것을 목표로 하였다. 방법 : 선행 연구의 매핑 가이드라인에서 제시된 7단계 과정을 수정하여 활용하였다. 매핑의 목적 및 범위 정의, 용어 추출, 개념 추출, 매핑을 위한 소스 용어 작업, SNOMED CT 개념 검색, 매핑 관계 분류 및 매핑 검증의 과정을 수행하였다. 매핑의 목적은 한의약 임상 아이디어를 표현하는 표준 용어로서 SNOMED CT를 평가하는 것이고, 범위에는 편두통 환자 관리의 평가, 진단, 치료 및 예방을 포함하였다. 결과 : 총 546개의 용어가 추출되었다. 중복된 용어를 제거한 후, 271개의 개념이 SNOMED CT 매핑에 사용되었다. 이중 43.2%는 SNOMED CT 개념과 의미론적으로 동등하게 매핑되었고(117개 개념), 39.1%는 SNOMED CT 개념이 더 포괄적인 의미를 가지도록 매핑되었다(106개 개념). 상대적으로 포괄적인 의미를 가지는 SNOMED CT 개념에 매핑된 한의약 개념 106개 중 19개는 SNOMED CT 후조합을 이용하여 의미론적으로 동등하게 표현이 가능하였다. 나머지 17.7%의 한의약 개념은 SNOMED CT에 매핑할 수 없었다. 결론 : 본 연구는 한의약에서 사용되는 개념을 SNOMED CT에 매핑하여 한의약 용어를 표준화하였다. 연구 결과를 바탕으로, 한의약에서 사용되는 용어를 표준의료용어로 표현하기 위하여 SNOMED CT에 새로운 개념과 속성을 추가하는 것을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.416-417
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• 2014

SNOMED CT is a standard clinical terminology to provide a standardized way for recording and managing of clinical records in EMR systems. However, because of its huge expressive power, it is very difficult to consistently record patients' status such as diagnosis and procedure. The reason is that one clinical meaning can be expressed in variety of ways using multiple terminologies and vice versa. This paper proposes a novel requirement of effective search browser for SNOMED CT terminologies by analyzing duplicated or similar terminologies.

• The Journal of the Korean dental association
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• v.24 no.11 s.210
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• pp.919-922
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• 1986

hypnosis(최면<술>)는 문자기록의 역사만큼이나 오래되었다. 1773년 Franz A.Mesmer는 의학분야에서 ‘최면술(mesmerism)'이란 용어를 사용하였으며, James Braid는 'hypnosis'란 용어를 최초로 사용하였고, Sigmund Frend는 치료의 한 형태로서 최면상태(hypnotic trance)를 연구하였으며, Hennon은 최과임상에서 사용되어지는 Hypnosis를 Hypnodontics라고 기술하였고, 실제 임상에 Hypnosis를 적용시키기 위해서는 따로 최면요법에 대한 수련을 쌓아야 한다고 하였다. 현재 임상에서 최면요법을 이용하고 있는 임상가는 그리 흔하지 않는 것 같으나 만약 습득만 한다면 행동조절의 한 방법으로서 유요하게 이용될 수 있으리라고 생각한다.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.871-881
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• 2010

Objective: A clinical contents model is an essential data model to exchange clinical data, among existing computer systems and enhance consistency of necessary data, in terms of its meaning and reusability. However, there has not been a domestic case where such clinical model is developed till present. Methods and Results: This research is based on determining principles of developing clinical information model which is a specified model of Health level 7 Reference Information Model and attempts to identify clinical contents with types of ENTITY-ATTRIBUTE-VALUE, based on terminology standard by clinicians and domain modelers. Conclusion: This model is projected to be utilized in the next generation of EMR as core contents.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.177-185
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• 2019

This is a methodological study to develop standards for human body coordinate clinical terminology system. The Standard was developed through four stages: Stage 1 - research plan was developed through literature review and data collection. Stage 2 - preliminary standard was created. Stage 3 - the standard was revised in accordance with the consultation of experts through seminars. Stage 4 - Final version of the standard was presented after verification of the content level. 2D human body images are expressed as x, y axes, and 3D images are expressed as x, y, z axes. Concepts and definitions of coordinates were preassembled into FSN, synonyms and preferred names of SNOMED CT. The latter combination was defined through 18 relationships. The average index was 4.01 for the content validity of the developed relationship standard. This research suggests that subsequent standards should be developed for other parts of the human body such as the brain, organs, and tissues. Also, it suggests that methodological research should be continued to increase the utilization of the standard in clinical practice.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.537-542
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• 2015

SNOMED CT, which is a standard clinical terminology, imposes an ambiguity problem of terminology selections caused by its huge expressive power and structural complexity. It is very difficult to distinguish similar terms and to select an appropriate term among them within short consultation hours. This paper analyzes the ambiguity problem and proposes a novel scheme for comparative search of similar terms. The proposed scheme provides a differential view of similar terms by defining a "is-not-a" relationship based on the hierarchical structure of the concepts. The result of this work improves the utilization of SNOMED CT such that medical officers can efficiently select an appropriate term among similar terms which represents patient's status adequately.