IDEAS home Printed from https://ideas.repec.org/a/igg/rmj000/v33y2020i2p1-18.html
   My bibliography  Save this article

A Cloud Framework Design for A Disease Symptom Self-inspection Service

Author

Listed:
  • Lu Yan

    (School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China)

  • Ding Xiong

    (School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China)

Abstract

The establishment of a symptom self-inspection service faces will face many challenges related to how to best acquire, store, and analyze the available data. In view of the problems, a cloud framework symptom self-inspection service model is proposed in this article. A Hadoop cluster is set up to store massive medical data and provide indexing so as to produce acceptable electronic medical record search response times. A cluster of the distributed search nodes based on Lucene can be used for real-time retrieval, data analysis, and privacy filtering from a massive collection of electronic medical records. The implementation of symptom check-up services is discussed, including the selection of search nodes, the establishment of medical records index files, the ranking and sorting of medical records similarity. Experimental results demonstrate that our proposed cloud framework model serves as a scalable and effective self-inspection health symptom service.

Suggested Citation

  • Lu Yan & Ding Xiong, 2020. "A Cloud Framework Design for A Disease Symptom Self-inspection Service," Information Resources Management Journal (IRMJ), IGI Global, vol. 33(2), pages 1-18, April.
    • Handle: RePEc:igg:rmj000:v:33:y:2020:i:2:p:1-18
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IRMJ.2020040101
    Download Restriction: no
    ---><---

    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:igg:rmj000:v:33:y:2020:i:2:p:1-18. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.