基于数据挖掘算法的慢病住院费用预测模型分析
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摘要
目的以研究医保住院费用报销的合理性为目的,分析慢性传染性疾病门诊费用结构、门诊治疗情况及患者静态特征等因素对住院费用的影响,并通过数据挖掘算法构建住院费用的预测模型。方法在构建模型的过程中,分别使用了决策树、Naive Bayes、Adaboost等算法,并比较了这些算法的准确度。结果以上采用的模型算法可以使得预测模型的准确度达到93%以上,能够较好地预测住院费用的合理性。肾衰竭/尿毒症的门诊特病费用与住院费用呈负相关趋势(r=-0.10,P<0.05),高血压、冠心病、糖尿病、脑血管病均呈正相关趋势(r=0.11、0.10、0.12、0.11,P<0.05)。结论根据不同类别疾病的特点,可以调整部分住院治疗项目到门诊,以实现对住院费用的调控。
Objective In order to analyze the rationale of the hospitalization cost,investigating the impact of outpatient cost structure,outpatient treatment situation,and static characteristics of patients on the hospitalization cost by establishing a data-mining model to predict the hospitalization cost.Methods In the process of building the model,the algorithms of decision tree,Naive Bayes,Adaboost were applied,and the accuracy of these above-mentioned algorithms were compared.Results The model could achieve an accuracy above 93%in the prediction of the hospitalization cost.The negative correlation existed between the outpatient fees for special diseases of renal failure/uremia and the hospitalization cost (r=-0.10,P<0.05),hypertension,coronary heart disease,diabetes,cerebrovascular disease showed positive correlation trend (r=0.11,0.10,0.12,0.11,P<0.05).Conclusion According to the characteristics of different diseases types,some inpatient treatment items can be adjusted to outpatient service to realize the regulation and control of the hospitalization cost.
Objective In order to analyze the rationale of the hospitalization cost,investigating the impact of outpatient cost structure,outpatient treatment situation,and static characteristics of patients on the hospitalization cost by establishing a data-mining model to predict the hospitalization cost.Methods In the process of building the model,the algorithms of decision tree,Naive Bayes,Adaboost were applied,and the accuracy of these above-mentioned algorithms were compared.Results The model could achieve an accuracy above 93%in the prediction of the hospitalization cost.The negative correlation existed between the outpatient fees for special diseases of renal failure/uremia and the hospitalization cost (r=-0.10,P<0.05),hypertension,coronary heart disease,diabetes,cerebrovascular disease showed positive correlation trend (r=0.11,0.10,0.12,0.11,P<0.05).Conclusion According to the characteristics of different diseases types,some inpatient treatment items can be adjusted to outpatient service to realize the regulation and control of the hospitalization cost.
引文
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[8]高金金,周伟,高咏,等.子宫肌瘤单病种住院费用[J].协和医学杂志,2016,7(2):152-155.
[9]杨金娟,高建民,许永建,等.恶性肿瘤化疗患者住院费用构成及影响因素分析[J].中国卫生经济,2014,33(9):35-38.
[2]王敏,张开金,姜丽,等.BP神经网络技术在慢性病患者住院费用研究中的应用[J].中国卫生经济,2010,29(11):86-88.
[3]宋蕾,张开金,包思敏,等.不同支付方式下慢性病患者住院费用对比分析[J].中国全科医学,2013,16(4):440-441.
[4]陶阳红,张开金,陈积瑞,等.参加城镇职工基本医疗保险的慢性病患者住院费用的影响因素调查[J].中国全科医学,2015,18(7):811-813.
[5]余震.杭州市基本医疗保险住院费用分析[D].杭州:浙江大学,2013.
[6]陶红兵.基于单病种管理的医院住院病人费用控制的管理策略研究[D].武汉:华中科技大学,2008.
[7]何倩,吴骋,王志勇,等.成分数据预测模型在住院费用构成中的应用[J].中国卫生经济,2013(3):27-29.
[8]高金金,周伟,高咏,等.子宫肌瘤单病种住院费用[J].协和医学杂志,2016,7(2):152-155.
[9]杨金娟,高建民,许永建,等.恶性肿瘤化疗患者住院费用构成及影响因素分析[J].中国卫生经济,2014,33(9):35-38.