中文摘要：

目的 分析影响天津市参保急性心肌梗死患者住院费用的因素指标,探索其病例组合方式及费用模式,为建立适合我国国情的DRGs研究提供统计学参考。方法 采用多重线性回归分析筛选影响因素,利用数据挖掘中的决策树方法建立模型并将患者归组分类。结果 是否手术、医院类别、住院天数为主要影响因素,并以此作为分割节点建立8种病例组合方式及相应的费用标准。结论 数据挖掘是一种从海量数据中提取有效信息的过程,应用于DRGs研究具备自身优势。两种方法证实影响AMI患者住院费用的因素及重要程度一致,是否手术为最主要的疾病诊断分割节点。通过得出的AMI患者费用控制上限可协助医疗保险机构及医院发现可疑病例,做好预警工作,完善社会医疗保险体系。

英文摘要：

Objective To analyze the influencing factors of insured inpatients with acute myocardial infarction in Tianjin, and then explore the case mix model and charge pattern in order to establish Diagnosis Related Groups （DRGs） suited to China＇s national conditions and provide statistical reference. Methods Adopt multinomial linear regression to screen the factors influencing hospitalization expenses, establishing the case mix model and decision tree were used to generate diagnosis related groups. Results The major influential factors were surgery or not,hospital＇s level and length of stay. There are 8 case mix and cost standard from decision tree. Conclusion Data mining is a process of extracting effective information from huge amounts of data, applied in DRGs research has its own advantages. Two methods confirm that the influence factors and significance of AMI inpatient＇s expense are the same,the uppermost splitting node is surgery or not in case mix model. The upper limit of medical charge can assist medical institutions in finding suspicious cases, early warning, and perfecting the social medical insurance system.