根据重庆市某三级甲等医院2012年1月至2014年12月5种常见疾病(糖尿病、甲状腺功能亢进、顺产、肠息肉、脑梗死)的月人均治疗费用数据,采用BP神经网络模型、广义回归神经网络模型、灰色GM(1,1)模型以及非线性回归模型,分别预测2015年1月至8月5种疾病的月人均治疗费用的变化情况,并与真实费用数据进行对比,判断4种模型预测的准确程度。结果表明:BP神经网络模型、广义回归神经网络模型、灰色GM(1,1)模型、非线性回归模型预测5种疾病的可决系数R²最小分别为0.278、0.565、0.048和0.097,最大分别为0.826、0.901、0.600和0.747;与2015年1月至8月的真实费用数据比较,4种模型预测的相对误差最小分别为9.845%、3.507%、5.897%和3.642%,最大分别为15.450%、13.940%、30.518%和17.204%。其中广义回归神经网络在疾病费用的预测结果相对于其他模型更准确。

On the basis of the per capita treatment cost per month of five common diseases, i.e. diabetes, intestinal polyp, hyperthyroidism, eutocia and cerebral infarction, in a 3-A-grade hospital in Chongqing from January, 2012 to December, 2014, we used BP neural network, generalized regression neural network (GRNN), grey system GM (1, 1) and non-linear regression analysis to predict the change of per capita treatment costs per month of these five diseases from January 2015 to August 2015. And the accuracy of these four models was judged by comparing the prediction results with real data. The results show that the minimum coefficients of determination (R²) of the four models are 0.278, 0.565, 0.048 and 0.097, respectively, while their maximum coefficients of determination(R²) are 0.826, 0.901, 0.600 and 0.747, respectively. The minimum prediction errors of the four models are 9.845%, 3.507%, 5.897% and 3.642%, respectively, while their maximum prediction errors are 15.450%, 13.940%, 30.518% and 17.204%, respectively. Compared with the other three models, the GRNN model can predict the cost of diseases more accurately.

重庆市科技计划项目资助(cstc2013jccxA10012)。