基于K-means聚类与支持向量机的大病患者住院费用影响因素与控制策略研究
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摘要
目的探寻有效方法分析大病患者住院费用的主要影响因素,为控制住院费用提供合理对策。方法样本选取2016年1月—2017年5月湖北省某市城镇居民基本医疗保险大病患者住院信息,采用K-means聚类和支持向量机进行分析。结果聚类优度检验提示将住院费用分为3类最佳,基于RBF核函数的支持向量机模型的预测准确度最高,住院费用的主要因素为主诊断疾病、住院日、医院级别、医保业务类别和医院类型。结论 K-means聚类与支持向量机模型可作为分析大病患者住院费用的有效方法,为控制住院费用提供策略。
Objective To explore methods by which the main influencing factors of hospitalization expenses of patients with serious diseases can be efficiently analyzed,and provide strategies for hospitalization expenses control. Methods Data of inpatients with Medical Insurance for Urban Residents from Jan. 2016 to May. 2017 were sampled in a city of Hubei. The data were analyzed by K-means clustering and support vector machine(SVM). Results Clustering goodness test indicated that the hospitalization expenses should be classified into three categories for best result; RBF-based SVM showed the highest prediction accuracy;the main influencing factors of hospitalization expenses included type of disease,length of stay,level of hospital,type of health care and type of hospital. Conclusion The K-means clustering and SVM model are effective in analyzing main influencing factors,according to which reasonable strategies for expenses control could be established.
Objective To explore methods by which the main influencing factors of hospitalization expenses of patients with serious diseases can be efficiently analyzed,and provide strategies for hospitalization expenses control. Methods Data of inpatients with Medical Insurance for Urban Residents from Jan. 2016 to May. 2017 were sampled in a city of Hubei. The data were analyzed by K-means clustering and support vector machine(SVM). Results Clustering goodness test indicated that the hospitalization expenses should be classified into three categories for best result; RBF-based SVM showed the highest prediction accuracy;the main influencing factors of hospitalization expenses included type of disease,length of stay,level of hospital,type of health care and type of hospital. Conclusion The K-means clustering and SVM model are effective in analyzing main influencing factors,according to which reasonable strategies for expenses control could be established.
引文
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[2]沈培.基于支持向量机的单病种医疗费用控制研究[J].中国卫生经济,2012,31(3):89-91.
[3]郭嫱,王文龙,杨洪滨.青光眼患者住院费用及影响因素分析[J].中国医院管理,2016,36(11):81-83.
[4]张磊,孔桂兰,马谢民.人工神经网络在糖尿病住院费用研究中的应用[J].中国医院管理,2015,35(1):61-64.
[5]国家卫生计生委.中国卫生统计年鉴[M].北京:中国协和医科大学出版社,2017.
[6]湖北省统计局.湖北统计年鉴[M].北京:中国统计出版社,2017.
[7]刘畅,周绿林,张心洁,等.重大疾病患者住院医疗费用影响因素实证研究[J].中国医院管理,2017,37(5):14-17.
[8]涂洪谊.不同等级医院医保老年患者住院费用分析[J].中国公共卫生,2009,25(8):1015-1016.
[9] CAI M,LIU E,TAO H,et al. Does a medical consortium influence health outcomes of hospitalized cancer patients? an integrated care model in Shanxi,China[J]. International Journal of Integrated Care,2018,18(2):1-10.
[10]朱洁,金萍妹,华伟,等. 15 348例宫颈癌不同治疗方式的住院费用研究[J].中国医院管理,2017,37(2):58-60.