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The design of regional medical cloud computing information platform based on deep learning
International Journal of System Assurance Engineering and Management Pub Date : 2021-03-05 , DOI: 10.1007/s13198-021-01075-1
Kaidong Zhang

In order to solve the imbalance of medical resources in different regions, a regional medical cloud computing information platform based on reactive algorithm is constructed. First, an application-oriented elastic scaling algorithm based on long short-term memory network and back propagation neural network (BPNN) is proposed. Then, based on cloud computing, a medical cloud data mining platform using Hadoop ecosystem is proposed. Finally, Visual Studio is used to develop regional medical cloud computing information platform, and the performance of the platform is tested. The experimental results show that the improved neural network algorithm has a loss and MAPE (mean absolute percentage error) value of 930 and 0.00031, respectively in the actual workload prediction, which is better than the algorithm before optimization. Moreover, it has the best fitting effect with the actual curve in the prediction of response time. In the strategy scheduling experiment, the loss of the model is 1.40222, the MAPE value is 0.34021, and the convergence time is 23 s, which is better than the test results of the model based on linear regression and BPNN. The experimental results suggest that the regional medical cloud computing information platform can solve the problem of unfair regional medical resources in the medical field to a certain extent.



中文翻译:

基于深度学习的区域医学云计算信息平台设计

为了解决不同地区医疗资源的不平衡,构建了基于反应算法的区域医疗云计算信息平台。首先,提出了一种基于长短期记忆网络和BP神经网络的面向应用的弹性缩放算法。然后,基于云计算,提出了一种使用Hadoop生态系统的医学云数据挖掘平台。最后,使用Visual Studio开发区域医疗云计算信息平台,并测试了平台的性能。实验结果表明,改进的神经网络算法在实际工作量预测中的损失和MAPE(绝对绝对百分比误差)值分别为930和0.00031,优于优化前的算法。而且,在预测响应时间时,它与实际曲线具有最佳拟合效果。在策略调度实验中,模型的损失为1.40222,MAPE值为0.34021,收敛时间为23 s,优于基于线性回归和BPNN的模型的测试结果。实验结果表明,区域医学云计算信息平台可以在一定程度上解决医学领域区域医学资源不公平的问题。

更新日期:2021-03-07
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