摘要
目的:探讨临床路径下病例组合方法和人工神经网络在病例组合中的应用。方法:利用某综合性医院的一个临床路径流程(腰椎间盘突出行椎板切除术或髓核摘除术)下的523份出院病历资料,采用K-MEANS聚类方法进行组合,用神经网络对预测病例的病例组合进行判断。结果:523份病历聚为4组,各组间费用95%可信区间互不重合;神经网络的训练误差为0.0 029,病例组合预测和判断符合率为98.91%。结论:以临床路径下产生的病例为单元样本进行病例组合,结果更科学、客观。神经网络用于病例组合判断,不用确定单个节点变量的分割值,更符合病例组合由多变量共同作用的实情。
Objective： To investigate the case-mix method by clinical pathway. Methods： K-MEANS cluster analysis was applied to case-mix classification and artificial neural network was used for case-mix prediction. Results： Five hundred and twenty three inpatient records constructed a case-mix classification scheme of 4 groups. Statistical significant difference of costs existed in 4 groups. The training error of artificial neural network was low （0.0 029 ） and the predicting result was accurate （98.91% ）. Conclusion： Case-mix result was more reasonable using records under clinical pathway. The existing models of case-mix depend on dividing individual variables, but artificial neural network does not.
出处
《医学研究生学报》
CAS
2007年第6期641-643,共3页
Journal of Medical Postgraduates
关键词
临床路径
病例组合
神经网络
Clinical pathway
Case-mix
Artificial neural network
