| Project/Area Number |
17390150
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Medical sociology
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| Research Institution | Gifu University |
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Principal Investigator |
KINOSADA Yasutomi Gifu University, Graduate School of Medicine, Professor, 大学院医学系研究科, 教授 (50161526)
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| Co-Investigator(Kenkyū-buntansha) |
SHIRATORI Yoshimune Gifu University, University Hospital, Associate Professor, 医学部附属病院, 助教授 (20313877)
TAKEUCHI Tomiko Gifu University, School of Medicine, Professor, 医学部, 教授 (40248860)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥12,100,000 (Direct Cost: ¥12,100,000)
Fiscal Year 2006: ¥4,700,000 (Direct Cost: ¥4,700,000)
Fiscal Year 2005: ¥7,400,000 (Direct Cost: ¥7,400,000)
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| Keywords | Electronic Medical Record System / Integrated Data Base / Clinical Process / Administrative Process / Data Mining |
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| Research Abstract |
The electronic medical record (EMR) systems are much more likely to be installed in hospitals. Many people know for certain that the large volume of clinical records and administrative data stored in the EMR systems are much valuable. But these data are still be left unused. This fact is a big problem and a new research issues in the field of biomedical informatics.
In this article, we are going to show the new environment of data mining and the relationship between the data mining system and the EMR system in the Gifu University Hospital. By applying data mining tools to clinical data, we can confirm the usefulness and effectiveness of the tools from a clinical view point.
Our main goal is to get to know the varies patterns of clinical processes performed in practice and evaluate the performance quantitatively in a hospital. Therefore, we defined the clinical process as a time-series of medical act for every patient, and applied the mining tool to the these clinical process data. The results were satisfactory and we could feel certain that the clinical process data stored in the EMR system could be possible to be analyzed quantitatively by using mining tools.
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