Risk Pre-warning Model of Doctor-Patient Relationship Based on Particle Swarm Optimization BP Neural Network
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摘要: 为了提高医患关系风险预警的准确度,提出一种基于粒子群优化反向传播(back propagation,BP)神经网络的医患关系风险预警模型. 首先采用通过粒子群算法优化BP神经网络初始权值和阈值的方法来提高BP神经网络的预测准确度;通过对模型优化前后的对比分析,得出优化后模型预测误差更小的实验结果. 仿真结果表明:此方法建立的医患关系风险预警模型收敛速度更快,预测精度更高.Abstract: In this paper, an optimized modeling method based on particle swarm optimization (PSO) toward back propagation (BP) neural network was proposed to raise the prediction accuracy for the doctor-patients risk pre-warning case. The PSO method was applied to optimize the initial weights and biases of the conventional BP neural network to raise the prediction accuracy. By contrast and analysis of the results, the optimized method achieved a more effective prediction with much lower error. Therefore, the proposed PSO-BP neural network provides a more promising prediction method with faster convergence and higher accuracy.
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图 5 基于粒子群优化BP模型实际输出与期望的线性回归
Figure 5. Actual output with the expected linear regression of the PSO-BP model
表 1 部分测试数据及结果
Table 1. Some of the test data and results
医方责任 患方责任 医疗行政管理问题 医疗行为过失 医疗行为不存在过失 … 期望值 实际输出 1 1 0 1 0 … 1 1 1 0 0 0 1 … 1 1 1 1 1 1 1 … 2 2 1 0 1 0 0 … 2 2 1 0 0 0 1 … 2 2 1 0 0 1 0 … 3 3 1 0 0 1 1 … 3 3 
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