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

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

Lu Yan, Ding Xiong
Copyright: © 2020 |Volume: 33 |Issue: 2 |Pages: 18
ISSN: 1040-1628|EISSN: 1533-7979|EISBN13: 9781799804741|DOI: 10.4018/IRMJ.2020040101
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MLA

Yan, Lu, and Ding Xiong. "A Cloud Framework Design for A Disease Symptom Self-inspection Service." IRMJ vol.33, no.2 2020: pp.1-18. http://doi.org/10.4018/IRMJ.2020040101

APA

Yan, L. & Xiong, D. (2020). A Cloud Framework Design for A Disease Symptom Self-inspection Service. Information Resources Management Journal (IRMJ), 33(2), 1-18. http://doi.org/10.4018/IRMJ.2020040101

Chicago

Yan, Lu, and Ding Xiong. "A Cloud Framework Design for A Disease Symptom Self-inspection Service," Information Resources Management Journal (IRMJ) 33, no.2: 1-18. http://doi.org/10.4018/IRMJ.2020040101

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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.

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