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Development of risk reduction for health promotion strategy based on data-mining

基于数据挖掘的健康促进战略降低风险制定

基本信息

批准号：
13470099
负责人：
YOSHIDA Katsumi
金额：
$ 5.12万
依托单位：
St. Marianna University School of Medicine
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-13470099/
关键词：
risk reduction data-mining neural network 健康課題評価健康評価リスク評価

项目摘要

Because of the recent change in disease structure in Japan, health policies such as Healthy Japan 21 or Health Promotion low were introduced for he prevention of lifestyle-related diseases. In this circumstance, not only screening to diseases but also risk reduction are expected to be valuable in health policy. Development of risk reduction model requires the early change of health status and evaluation of longitudinal change. However, the ordinary risk analysis methods have the limitation for the detection of the early changes.This study conducted the non-linear methods based on data-mining for assessment of the risk reduction strategies. Genetic algorism and immune-system algorism were also included in this projects. Comparison of epidemiological analysis such as proportinal hazard model, neural network modes were performed for the etiological analysis for ICU acquired infections. Aggregate single point model, multiple point model and proportional hazard model are compared in the area under the curve of ROC and classification accuracy. This result showed the multiple point model were superior to proportional hazard model and aggregate single point model. For development of risk reduction model, non-linear model seems to be appropriate for data-mining strategies. The cimposition of the database also seems to be important for the proper knowledge acquisition. Concordance index proposed by Harell et al will be matter under consideration for the comparison of the priority to some models.

由于最近日本疾病结构的变化，为预防与生活方式有关的疾病，推出了健康日本21或健康促进等健康政策。在这种情况下，不仅要筛查疾病，而且要降低风险，这在卫生政策中是有价值的。风险降低模型的发展需要健康状况的早期变化和纵向变化的评估。然而，普通的风险分析方法在早期发现风险变化方面存在局限性，本研究采用基于数据挖掘的非线性方法对风险降低策略进行评估。遗传算法和免疫系统算法也包括在这个项目中。比较了比例风险模型、神经网络模型等流行病学分析方法在ICU获得性感染病原学分析中的应用。比较了聚集单点模型、多点模型和比例风险模型的ROC曲线下面积和分类准确率。结果表明，多点模型上级比例风险模型和聚集单点模型。对于风险降低模型的发展，非线性模型似乎是合适的数据挖掘策略。数据库的建立似乎对正确的知识获取也很重要。Harell等人提出的一致性指数将被考虑用于与某些模型进行优先级比较。