| Project/Area Number |
13680927
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
OHE Kazuhiko The University of Tokyo, Faculty of Medicine, Professor, 医学部附属病院, 教授 (40221121)
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| Co-Investigator(Kenkyū-buntansha) |
ONOGI Yuzo The University of Tokyo, Faculty of Medicine, Associate Professor, 医学部附属病院, 助教授 (90233593)
WATANABE Hiroki The University of Tokyo, Faculty of Medicine, Research Associate, 医学部附属病院, 助手 (10334377)
HATANO Kenji The University of Tokyo, Faculty of Medicine, Research Associate, 医学部附属病院, 助手 (60311619)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 2002: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2001: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | medical information processing / clinical datavase / medical thesaurus / admission report / standardization of disease names / data mining |
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| Research Abstract |
Purpose : development of 1)automatic extraction of significant clinical terms contained in computer-based admission report of patients. 2)representation of clinical meaning of cases with keeping semantic network of relationships. among medical terms extracted, 3)extraction of similar cases from the database, 4)evaluation of the efficiency of the above methods using cross-validation methods.
Methods : For the preparation of developing a compute-based admission reporting system, we analyzed the semantic structure of the extracted medical terms, and constructed a thesaurus database of medical terms extracted from clinical, report and medical records. Further, we developed a standardized clinical terminology of diseases and constructed the structured hierarchy of the terminology. Finally we developed database of an admission reports for the evaluation study and extracted the relations of medical terms.
Results : We constructed a large relational semantic structure that contains about 20000 disease names and 60000 indexing terms. We analyzed 800 admission reports using this database and extracted significant medical terms and co-occurrence graph. Using validation method, we evaluated the results and 17 cases were good and 3 cases were evaluated as poor cases.
Discussion: The main contribution of this study was that we constructed the semantic database of medical terminology for the future study for natural language processing of medical text-base. We have to add terminology of signs and symptoms. Concerning automatic extraction of similar cases, whether a sign or a symptom exists or not is the most significant information and constructing the necessary database for the purpose is the future issue to be achieved
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