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The design of regional medical cloud computing information platform based on deep learning

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  • Kaidong Zhang

    (Beijing Jiaotong University)

Abstract

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.

Suggested Citation

  • Kaidong Zhang, 2021. "The design of regional medical cloud computing information platform based on deep learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 757-764, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01075-1
    DOI: 10.1007/s13198-021-01075-1
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