| Project/Area Number |
18590499
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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 |
Medical sociology
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| Research Institution | Tokyo Women's Medical University |
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Principal Investigator |
SAKURA Hiroshi Tokyo Women's Medical University, School of Medicine, Diabetes Center, Associate Professor (70240710)
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| Co-Investigator(Kenkyū-buntansha) |
KANNO Hiroko Tokyo Women's Medical University School of Medicine, Diabetes Center, Assistant Professor (90338971)
MARUYAMA Satoko Tokyo Women's Medical University School of Medicine, Diabetes Center, Assistant Professor (60318061)
IWAMOTO Yasuhiko Tokyo Women's Medical University School of Medicine, Diabetes Center, Professor (60143434)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,830,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2006: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | diabetes mellitus / electric medical record system / datamart / data mining / database / medical information / information technology / 医療情報学 / 情報システム |
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| Research Abstract |
We have integrated the clinical information such as clinical history, physical examinations and laboratory data into a hospital-based datamart for the analysis of clinical course of diabetes mellitus. Using the template in the electronic medical recored system, we could effectively integrated medical information into the datamart. The number of total records put in the datamart was more than 10 million, and using the structured query language (SQL), we could easily extract the necessary data.
We presented the data entitled "Identification of factors predicting glycemic control in new patients with type 2 diabetes", "Factors associated with glycemic control after an inpatient program", "Seasonal fluctuation of glycosylated hemoglobin in Japanese diabetic patients" et al. in the international meeting.
We also analyzed the factors which predict glycemic control 6 months after the admission to the inpatient clinic using data-mining methods (association rules, decision-tree methods, neural network etc.). We found that data-mining techniques are more powerful methods to analyze complicated disease like diabetes mellitus than conventional multivariate analysis.
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