基于BP神经网络和D-S证据理论的医院审计预警决策研究

Research on Early Warning and Decision Making of Hospital Audit Based on BP Neural Network and D-S Evidence Theory

为精确估计医院审计预警度,提出了基于BP神经网络与D-S证据理论的医院审计预警决策的融合方法。利用D-S证据理论与BP神经网络作为医院决策审计报警识别的手段[1-3]。通过模拟输入各种不同种类的数据进行仿真,发现该理论可以显著提高审计预警的识别能力,有效降低审计预警误报率。将该理论应用到医院审计系统中,具有适应性并达到了预期的效果。

A fusion method based on BP neural network and D-S evidence theory is put forward in this paper.Use D-S evidence theory and BP neural network as a means to identify the hospital decision-making audit alarm.Through the simulation of various kinds of data,it is found that the theory can significantly improve the recognition ability of the audit warning,and effectively reduce the false alarm rate.