Cloud Health Resource Sharing Based on Consensus-Oriented Blockchain Technology: Case Study on a Breast Tumor Diagnosis Service.

Background: In recent years, researchers have made significant efforts in advancing blockchain technology. This technology, with distinct features of decentralization and security, can be applied to many fields. In areas of health data and resource sharing, applications of blockchain technology are...

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Publicado en:Journal of Medical Internet Research Vol. 21; no. 7
Autores principales: Zhu, Xiaobao, Shi, Jing, Lu, Cuiyuan
Formato: Journal Article
Publicado: JMIR Publications Inc. Jul2019
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2019
      vid: 21
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        atl: Cloud Health Resource Sharing Based on Consensus-Oriented Blockchain Technology: Case Study on a Breast Tumor Diagnosis Service.
      aug:
        au:
          Zhu, Xiaobao
          Shi, Jing
          Lu, Cuiyuan
        affil: School of Software, Nanchang Hangkong University, Nanchang, China
      sug:
      ab: Background: In recent years, researchers have made significant efforts in advancing blockchain technology. This technology, with distinct features of decentralization and security, can be applied to many fields. In areas of health data and resource sharing, applications of blockchain technology are also emerging.Objective: In this study, we propose a cloud health resource-sharing model based on consensus-oriented blockchain technology and have developed a simulation study on breast tumor diagnosis.Methods: The proposed platform is built on a consortium or federated blockchain that possesses features of both centralization and decentralization. The consensus mechanisms generate operating standards for the proposed model. Open source Ethereum code is employed to provide the blockchain environment. Proof of Authority is selected as the consensus algorithm of block generation.Results: Based on the proposed model, a simulation case study for breast tumor classification is constructed. The simulation includes 9893 service requests from 100 users; 22 service providers are equipped with 22 different classification methods. Each request is fulfilled by a service provider recommended by the weighted k-nearest neighbors (KNN) algorithm. The majority of service requests are handled by 9 providers, and provider service evaluation scores tend to stabilize. Also, user priority on KNN weights significantly affects the system operation outcome.Conclusions: The proposed model is feasible based on the simulation case study for the cloud service of breast tumor diagnosis and has the potential to be applied to other applications.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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