支持向量机模型在肺癌病人住院费用影响因素分析中的应用  

Application of the support vector machine model in the analysis of impact factors for hospitalization expenses

作　　者：张颖[1] 李利杰[2] 刘海容[3] 朱绥 孙统达[1] 

机构地区：[1]宁波卫生职业技术学院,宁波市鄞州区学府路51号315104 [2]宁波城市职业技术学院,宁波市鄞州区学府路9号315104 [3]宁波市第一医院,宁波市柳汀街59号315010 [4]宁波市鄞州区第三医院

出　　处：《中国医院》2014年第10期30-32,共3页Chinese Hospitals

基　　金：2013年浙江省医药卫生科技计划项目(2013KYB242);浙江省卫生经济学会资助课题

摘　　要：目的:建立基于支持向量机的单病种住院费用拟合模型,利用模型分析住院费用影响因素及其对住院费用的影响程度。方法:以浙江省某三级甲等医院2010年-2013年间肺癌患者的住院信息为样本,利用SPSS 16.0建立数据库,应用Weka软件建立支持向量机拟合模型,分析住院费用的影响因素。结果:影响住院费用的主要因素依次为住院天数、主诊断疾病、麻醉方式、年龄、费用类别、职业、住院次数。结论:从缩短住院天数、发展全民基本医疗保障制度等方面来控制住院费用。Objective： To construct the single disease hospitalization expenses fitted model based on support vector machine and analyze the impact factors and the influence degree of the impact factors for hospitalization expenses. Methods： Data were collected from the information of inpatient records of lung cancer patients offered by a tertiary hospital of Zhejiang province from 2010 to 2013. The database was created by SPSS 16.0 and the support vector machine model was constructed by Weka software for analyzing the factors of affecting hospitalization expenses. Results： The main factors affecting hospitalization expenses are length of stay, the main diagnosis, anesthesia method, age, type of payment, occupation and number of hospital admission. Conclusion： Hospital expenses can be reduced by shortening length of stay and promoting universal coverage.

关 键 词：肺癌病人 住院费用 支持向量机模型 

分 类 号：R197.1[医药卫生—卫生事业管理]