This article describes the activity of the "Medical termonology practical committee (MTPC) of Korean Medical Association (KMA). In 2001, MTPC of KMA published the 4th edition of "English-Korean, Korean-English Medical terminology" which contains about 5,000 medical terms. The MTPC will continue reviewing the vocabularies. Obsolete terms will be discarded, new terms will be added, and the remainder will be revised by through-going revision of vocabulary. Our aim is to reflect the current usage of medical terms in the form of easily understandable Korean terms.
Vocabulary

The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics (OHDSI) initiative combines over 100 controlled vocabularies into its own. However, the OHDSI vocabulary is limited in the sense that it combines multiple terminologies and does not provide a direct way to link them outside of their own self-contained scope. This issue makes the tasks of enriching feature sets by using external resources extremely difficult. In order to address these shortcomings, we have created a linked data version of the OHDSI vocabulary, connecting it with already established linked resources like bioportal, bio2rdf, etc. with the ultimate purpose of enabling the interoperability of resources previously foreign to the OHDSI universe.
Informatics ; Medical Informatics ; Vocabulary ; Vocabulary, Controlled

OBJECTIVES: The purposes of this study were to identify the difference between consumer vocabulary and medical vocabulary in terms of health information; to understand the features of consumer vocabulary; and to contribute by building a system that is able to link consumer vocabulary with medical vocabulary. METHODS: Data collection was conducted using articles in the knowledge corner of a portal web-site. A total of 43,304 health-related terms (total terms extracted) were collected as objects of this study and these terms were analyzed for their mapping rate and frequency of use (the repeated number of a term). RESULTS: The rate of mapping between the consumer vocabulary for health information and the medical vocabulary was not high. However, the number of "unmapped terms" was decreased by linking terms having similar forms to "preferred terms" and by extending synonyms. CONCLUSION: Linking with preferred terms and extending synonyms are, thus, required to increase the mapping rate between consumer vocabulary for health information and medical vocabulary, and the terms that consumers use are essential to further be researched in order to understand their morphology and features; hence, increasing consumer accessibility to the medical field.
Data Collection ; Vocabulary*

Speech recognition as an input tool for electronic medical record enables efficient data entry at the point of care. We evaluated the speech recognition accuracy of IBM ViaVoiceTM for doctor-patient dialogues and for pronounced medical vocabularies. The recognition accuracy for doctor-patient dialogues was 95.4%, while that for pronounced medical vocabularies was 55.1%. In order to put speech-based electronic medical record to practical use, mis-recognized vocabulary must be significantly corrected. This paper describes a correction system for mis-recognized medical vocabulary for speech recognition-enabled electronic medical record. The correction system is composed of an extraction and a correction steps. In the extraction step, hamming distance between a parsed substring and the nearest medical vocabulary in the vocabulary database greater than 50% of the length of the substring was used to determine if the substring is a possible mis-recognized medical vocabulary. In the correction step, possible mis-recognized medical vocabularies are scored such that when both the code and location of a syllable is the same with those of a medical vocabulary found in our database, +5 is given and when the code is the same but the location is not, +1 is given. The medical vocabulary with the highest score in the database is used as the correction for the mis-recognized one. When 33 patient-doctor dialogues with 33 medical vocabularies were tested for three times by six testees (i.e., 33 x 6 x 3 = 594 sentences), 94% of the mis-recognized words were correctly detected and repaired. Poor recognition performance for hard medical vocabularies can be markedly improved by the mis-recognized medical vocabulary correction system.
Electronic Health Records* ; Vocabulary*

OBJECTIVES: As interest in the field of affective science continues to increase, research into the arousal of emotions by the use of facial stimuli, event pictures, and stimulus words is now being actively pursued. The purpose of this study was to develop a Korean Affective Word List for eliciting emotional reactions. METHODS: The preliminary selection process was more carefully divided into the primary process when the words were extracted which the author thought elicited the emotions of happiness, sadness, fear, anger, and disgust from the Korean-Language Dictionary according to vocabulary frequency, the secondary process when the words were extracted which the Affective Words Selection Committee judged elicited only a single category of emotion. The affective words selected in the two-stage preliminary process were then presented to normal, young subjects, who were asked to allocate each word on the basis of their emotional reaction to one of the following emotional categories: happiness, sadness, fear, anger, disgust, and surprise. After the selected words caused the intended-emotional response with inter-rater agreement in more than 80%, a total of 166 words were selected except surprise. The complementary selection process was carried out following the preliminary process in order to make up for the lack of surprise words and the relative want of anger words. RESULTS: A total of 184 words were finally selected: 83 words for happiness, 36 for sadness, 24 for fear, 10 for anger, 20 for disgust, and 11 for surprise. CONCLUSION: These Korean affective words are expected to be widely used for eliciting emotions in future Korean research on emotion.
Anger ; Arousal ; Happiness ; Vocabulary

In order to maintain a uniformity and consistency in terminology for constructing and searching a literature database, controlled vocabularies should be used as key words in the journal. While most of medical academic societies have recommended MeSH be used as key words, only 23 societies published terminology or index books. We reviewed 172 journals of JKOSMI published during the period from 1995 to 2000, using MeSH brower. Only 11.72% of key words were completely consistent with MeSH terminolgy and 25.56% were partially consistent. Purposes of this study are to examine a current status of using MeSH in the Journal of Korean Society of Medical Informatics(JKOSMI) and to develop a Korean terminology book on medical informatics.
Classification ; Medical Informatics* ; Vocabulary, Controlled

The purpose of this article was to determine the discriminant function analysis of the Korean Wechsler Intelligence Scale (KWIS) for 110 normal controls and 98 schizophrenics. Of special interest was to verify the clinical discriminant power of two subtests of the KWIS (Vocabulary and Digit Symbols) and Zung's Self-rating Anxiety Scale (SAS). Four major hypotheses were postulated. The normal control group would show higher scores then the schizophrenics; mean scores on both Vocabulary and Digit Symbol. The mean difference in Digit Symbol between the two groups would be greater than that in the Vocabulary. There would be no significant relation among Digit Symbol, Vocabulary, and Anxiety. The most powerful discriminant power would be expected from subtest of Digit Symbol. The mean discriminant scores were 1.34425 for the control subjects, 1.34425 for the schizophrenics. The correctly discriminated percentage was 89.1% for the control subjects, 90.8% for the schizophrenics. From the findings it was concluded that both Digit Symbol and Vocabulary scales had strong diagnostic value but the former was more powerful than the latter. However, the Anxiety scales had less diagnostic value.
Anxiety ; Humans ; Intelligence ; Vocabulary ; Weights and Measures

OBJECTIVE: The standard vocabularies need to cover a diverse and enriched field of medical content, thereby facilitating semantic information retrieval, clinical decision support and efficient care delivery. SNOMED-CT(Systematized Nomenclature of Human and Veterinary Medicine-Clinical Term) is a comprehensive and precise clinical reference terminology that provides unsurpassed clinical content and expressivity for clinical documentation and reporting. To investigate whether the SNOMED-CT can serve this function in Seoul National University Hospital(SNUH) environment, we evaluated the coverage of SNOMED-CT as compared with clinical terms in the discharge summary at SNUH. METHODS: We tested for discordance of clinical terms between SNUH discharge summary and those from SNOMED-CT. We extracted 9,554 concepts from 1,000 discharge summaries. From these concepts, we obtained 3,545 unique concepts which are normalized to map with SNOMED-CT. These normalized terms are mapped to concepts of SNOMED-CT with semi-automatic method. RESULTS: We found a degree of concordance between SNOMED-CT and the clinical terms used in the discharge summary. Approximately, 89% of medical terms in the discharge summary are matched and 11% of the concepts are not mapped to those of SNOMED-CT. CONCLUSION: Through this study, we confirmed that SNOMED-CT is appropriate reference terminology in SNUH environment.
Humans ; Information Storage and Retrieval ; Semantics ; Seoul ; Vocabulary

Based on the idea of the ontology, knowledge representation and structure of knowledge base of clinical cases is proposed. The knowledge acquisition process of clinical cases is introduced, the methods of clinical case similarity calculation is proposed; and the experiments of case similarity calculation has been carried on using clinical data calculation is proposed; and the experiments of case similarity calculation has been carried on using clinical data from hospital.
Artificial Intelligence ; Humans ; Knowledge Bases ; Vocabulary, Controlled

OBJECTIVE: To Implement Effective Vocabulary Viewer for Electronic Medical Vocabulary Database Such as MED. DESIGN: A few Medical Vocabulary Database access method are reviewed. Some of their disadvantage of acquiring specific medical concepts are identified. MED has chosen as an example Medical Vocabulary System. We studied AccessMED, a vocabulary browser that supports lexical matching and the traversal of hierarchical and semantic links, and enhanced methodology of medical vocabulary browsing and acquiring medical vocabulary. RESULTS: The paper suggests platform independent client-server architecture Medical Vocabulary Viewer, MEDUSA. Additional support for Medical Vocabulary Browsing options such as display all ancestor, multiple concepts displaying, and bookmarking workspaces are implemented. CONCLUSION: Since MEDUSA uses client-server architecture along with scripting language such as TCL/TK as implementation language, it runs multiple platform. We claim MEDUSA can be easily integrated with other Medical Vocabulary such as UMLS. Using bookmark and annotation function of MEDUSA enables many vocabulary developers from multiple sites to communicate and discuss new concepts better.
Semantics ; Unified Medical Language System ; Vocabulary*