BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacy

7.项目摘要/摘要 随着电子健康记录系统的广泛采用，跨机构基因组医学预测 建模正变得越来越重要，并且有可能使可泛化的模型能够 加快研究和促进质量改进举措。例如，了解一个 特定的变量具有临床意义取决于多种因素，其中一个重要的因素是统计学 该变异与临床表型之间存在显著的相关性。预测的多变量模型 在接受某些治疗药物后的疾病倾向或结果可以帮助推动基因组 将医学纳入主流临床护理。然而，现有的大多数隐私保护机器学习方法 在给定临床数据的情况下用于构建预测模型的模型基于集中式架构，该架构 呈现安全和健壮性漏洞，如单点故障。 在这项提案中，我们将开发新的方法来分散隐私保护基因组医学预测 建模，它可以促进比较有效性研究、生物医学发现和患者护理。我们的 第一个目标是开发一个关于私有区块链网络的预测建模框架。这一目标依赖于 区块链技术和共识协议，以及在线和批处理机器学习算法， 为进一步提供基于区块链的开源隐私保护预测建模库 区块链相关研究和应用。我们将描述区块链技术提供的设置 超越现有技术的进步。第二个目标是开发基于区块链的隐私保护 用于真实世界临床数据研究网络的基因组医学建模架构。这些目标都是致力于 为国家人类基因组研究所(NHGRI)发展生物医学技术的使命 具有基因组学和医疗保健的应用领域。 NIH独立之路奖为申请者提供了一个极好的机会来补充他的 具有计算机科学背景，具有生物医学知识，并接受过机器学习和 以知识为基础的系统。它还将使他能够研究新的技术，以推进基因组和 医疗保健隐私保护。拟议项目的成功将有助于他获得 美国一所主要研究型大学生物医学信息学专业的教员职位 独立资助的去中心化隐私保护计算领域的研究。

7. Project Summary/Abstract With the wide adoption of electronic health record systems, cross-institutional genomic medicine predictive modeling is becoming increasingly important, and have the potential to enable generalizable models to accelerate research and facilitate quality improvement initiatives. For example, understanding whether a particular variable has clinical significance depends on a variety of factors, one important one being statistically significant associations between the variant and clinical phenotypes. Multivariate models that predict predisposition to disease or outcomes after receiving certain therapeutic agents can help propel genomic medicine into mainstream clinical care. However, most existing privacy-preserving machine learning methods that have been used to build predictive models given clinical data are based on centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure. In this proposal, we will develop novel methods for decentralized privacy-preserving genomic medicine predictive modeling, which can advance comparative effectiveness research, biomedical discovery, and patient-care. Our first aim is to develop a predictive modeling framework on private Blockchain networks. This aim relies on the Blockchain technology and consensus protocols, as well as the online and batch machine learning algorithms, to provide an open-source Blockchain-based privacy-preserving predictive modeling library for further Blockchain-related studies and applications. We will characterize settings in which Blockchain technology offers advances over current technologies. The second aim is to develop a Blockchain-based privacy-preserving genomic medicine modeling architecture for real-world clinical data research networks. These aims are devoted to the mission of the National Human Genome Research Institute (NHGRI) to develop biomedical technologies with application domain of genomics and healthcare. The NIH Pathway to Independence Award provides a great opportunity for the applicant to complement his computer science background with biomedical knowledge, and specialized training in machine learning and knowledge-based systems. It will also allow him to investigate new techniques to advance genomic and healthcare privacy protection. The success of the proposed project will help his long-term career goal of obtaining a faculty position at a biomedical informatics program at a major US research university and conduct independently funded research in the field of decentralized privacy-preserving computation.