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Medical vocabularies in medical records are used in several synonyms and various expressions even though they are same concepts. Query expansion using a thesaurus enhances recall of medical information retrieval (IR) system for searching patient records or literatures. This study proposed IR system architecture applied the Metathesaurus of Unified Medical Language System (UMLS). To enhance the retrieval effectiveness at the same time to reduce retrieval time, we reconstructed condensed Metathesaurus (CMT), which is constituted of frequently used terms in medical records. We used 40,000 radiology reports of Brain CT/MRI at Seoul National University Hospital. The retrieval model we used is the Boolean methods. The results showed 15~27% effectiveness for searching relevant documents implementing the UMLS MT into IR system for query expansion. But it took 3.5 times longer for retrieval compared with non-MT implemented IR system. When we applied the CMT into IR system, however, the retrieval time reduced by 50% and the retrieval performance decreased only 8.7% compared with all MT implemented IR system. In this paper, we developed the medical document retrieval system applied UMLS MT for query expansion methods that can improve the relevant document retrieval performance, at the same time it can reduce the retrieval time through consisting condensed Metathesaurus for a specific domain.
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. (Journal of Korean Society of Medical Informatics 11-3,265-272, 2005)