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Analysis of diabetic medical information using the datamart of electronic medical record system

利用电子病历系统数据集市分析糖尿病医疗信息

基本信息

批准号：
18590499
负责人：
SAKURA Hiroshi
金额：
$ 2.45万
依托单位：
Tokyo Women's Medical University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18590499/
关键词：
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

项目摘要

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.

我们将临床病史、体格检查、实验室数据等临床信息整合到以医院为基础的数据集市中，用于分析糖尿病的临床病程。在电子病历系统中使用该模板，可以有效地将医疗信息整合到数据集市中。放在数据集市中的记录总数超过1000万条，使用结构化查询语言（SQL），我们可以轻松地提取必要的数据。我们在国际会议上发表了题为“识别预测2型糖尿病新患者血糖控制的因素”、“住院后血糖控制的相关因素”、“日本糖尿病患者糖化血红蛋白的季节性波动”等数据。采用数据挖掘方法（关联规则法、决策树法、神经网络法等）分析住院患者入院后6个月血糖控制的预测因素。我们发现数据挖掘技术是比传统的多变量分析更有效的分析糖尿病等复杂疾病的方法。