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Transform Blockchain into Distributed Parallel Computing Architecture for Precision Medicine

机译：将区块链转换为Precision Medicine的分布式并行计算架构

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摘要

This paper provides a vision and proposes mechanisms to transform the blockchain duplicated computing into distributed parallel computing architecture by transforming smart contract which features data driven from the ground up to support moving computing to native data strategy. This new distributed parallel computing architecture can be employed to build a large size of data set from various distributed hosted medical data sets which might consist of personal electronic medical record (EMR) and various medical data. This large medical data set will enable researchers to jump start the deep learning research for medical domain. Distributed data management, distributed data sharing, and distributed learning are the core mechanisms in the new architecture. The required new researches and developments to employ Google federated learning and transfer learning algorithms in this new architecture are discussed. The approach and mechanism enabled by the new architecture is illustrated to build a real world evidence of clinical trial toward personal and precision medicine. Research issues and technical challenges are provided.
机译：本文提供了一种愿景，并提出了通过转换从地下驱动的智能合同来将区块链复制计算转换为分布式并行计算架构的机制，该智能合同来支持移动计算到本机数据策略。该新的分布式并行计算架构可以用于构建来自各种分布式托管的医疗数据集的大尺寸数据集，该数据集可能由个人电子医疗记录（EMR）和各种医疗数据组成。这套大型医疗数据集将使研究人员能够开始启动医疗领域的深度学习研究。分布式数据管理，分布式数据共享和分布式学习是新架构中的核心机制。讨论了在这种新架构中使用谷歌联合学习和转移学习算法的所需的新研究和发展。通过新架构实现的方法和机制，以建立对个人和精密医学的临床试验的真实信息。提供了研究问题和技术挑战。

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