基于时间序列模型的医院门诊量分析与预测

Hospital outpatient visit analysis and forecasting using time series models

医院门诊量分析与预测对医疗资源管理和为高质量医疗护理提供决策有重要作用.当前在门诊量分析与预测方面的研究还没引起足够重视,且研究主要集中在门诊量预测的计算方法,缺少全面深入的数据分析和规律挖掘.为此提出构建ARMAX模型、神经网络模型和ARMAX模型与神经网络的混合模型,用来描述医院门诊量的线性和非线性特征.以时间序列模型全面深入地分析厦门市医院门诊量日度数据的规律,研究发现,医院门诊量有显著的上升趋势、周内日效应以及很强的序列自相关性.通过样本外预测比较发现,采用混合模型进行预测取得的预测结果较好,这是由于混合模型能够同时获取门诊量数据的线性部分和非线性部分,数据信息比较完整.

Analysis and forecasting of hospital outpatient visits are important in making correct and feasible decisions for hospital resources management and high quality patient care provision.However,research in outpatient visit analysis and forecasting has not drawn much attentions so far,and current research mainly focuses on the computational methods for forecasting only,lacking in comprehensive analysis,rules finding,and knowledge discovery for hospital outpatient visits.Thus it was propsed to construct autoregressive moving average models(ARMAX),neural network models,and hybrid models integrating ARMAX and NN for outpatient visit analysis and forecasting.By constructing these models,the rules of the daily outpatient visit of the Xiamen city,China were analyzed comprehensively.It was fund that outpatient visit data show a significantly upward time trend,a significant day-of-week effect,and a significant serial autocorrelation.By comparing the forecasting performance of these time series models,itwas fund that the ARMAX+NN hybrid model achieves better performance,which is mainly due to the fact that the hybrid model can capture both linear and nonlinear parts of the outpatient visit data.