IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/2083416.html
   My bibliography  Save this article

Computer Management Design and Optimization of City Smart Medical Laboratory Service

Author

Listed:
  • Xiangdong Jin
  • Xia Zhang
  • Tianli Fan
  • Yinsen Song

Abstract

In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.

Suggested Citation

  • Xiangdong Jin & Xia Zhang & Tianli Fan & Yinsen Song, 2021. "Computer Management Design and Optimization of City Smart Medical Laboratory Service," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, November.
  • Handle: RePEc:hin:jnlmpe:2083416
    DOI: 10.1155/2021/2083416
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/2083416.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2021/2083416.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/2083416?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:2083416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.