arXiv 1802.017462018-02-06cs.CYcs.CR

ModelChain：私有区块链网络上的去中心化隐私保护医疗保健预测建模框架

ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks

Tsung-Ting Kuo, Lucila Ohno-Machado

摘要：

跨机构医疗保健预测建模可以加速研究并促进质量改进计划，因此对于国家医疗保健提供优先事项非常重要。例如，如果使用来自多个机构的数据开发预测特定患者组的再入院风险的模型，则该模型将更具有普遍性。虽然存在构建预测模型的隐私保护方法，但大多数都基于集中式架构，这会带来安全性和鲁棒性漏洞，例如单点故障（和单点漏洞）以及意外或恶意修改记录。在这篇文章中，我们描述了一个新的框架，ModelChain，以适应区块链技术的隐私保护机器学习。每个参与站点都有助于模型参数估计，而不会泄露任何患者健康信息（即，各机构之间只交换模型数据，不交换观测数据）。我们将隐私保护在线机器学习与私有区块链网络相结合，应用交易元数据来传播部分模型，并设计了一种新的信息证明算法来确定在线学习过程的顺序。我们还讨论了应用区块链技术解决隐私保护医疗保健预测建模任务的好处和潜在问题，并提高机构之间的互操作性，以支持全国互操作性路线图和国家医疗保健提供优先事项，如以患者为中心的成果研究（PCOR）。

英文摘要：

Cross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts risk of re-admission for a particular set of patients will be more generalizable if developed with data from multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each participating site contributes to model parameter estimation without revealing any patient health information (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-preserving online machine learning with a private Blockchain network, apply transaction metadata to disseminate partial models, and design a new proof-of-information algorithm to determine the order of the online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities such as Patient-Centered Outcomes Research (PCOR).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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