摘要
目的:探讨影响原发性肺癌发病危险因素,构建疾病预测模型,为肺癌预防和疾病控制提供依据。方法:基于大数据理念,通过构建医疗大数据中心层,整合分散临床数据,针对医院不同的业务需求提供给各专题数据中心。在数据应用方面,采用单因素和多因素条件Logistic回归模型进行分析,通过主成分法对主要危险因素提取公因子,并对公因子进行条件Logistic回归拟合分析。结果及结论:提出了一种基于Logistic回归分析的疾病预测模型构建方法,可进一步建立系统,突出临床应用价值。
Objective: In order to give a case for lung cancer prediction and its' control, the paper is mainly to build the disease prediction model by discussing the dangerous factors influenced lung cancer. Methods: According to the big data management concept, we construct the medical data platform, which can provide special data center adapting to the varieties of business requirements of hospital, by integrating decentralized medical data. In the aspect of data application, one-way anovalogistic regression and multi-way anovaregression were both studied by the paper. We analyzed the common factor, which we picked up from the dangerous factors by principal component analysis, by logistic regression. Results & Conclusion: A disease prediction model was presented by the logistic regression, which can be achieved by the system in future for the better clinical applying.
出处
《中国数字医学》
2018年第3期16-18,共3页
China Digital Medicine
基金
十三五南京市卫生青年人才培养工程项目(编号:QRX17184)~~