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

BECKON - 区块估算链：按需创建知识

• 批准号: 10133117
• 负责人: Tsung-Ting Kuo
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10133117
• 关键词: Adoption; Algorithms; Architecture; Authorization documentation; Award; Biomedical Technology; Caring; Characteristics; Client; Clinical; Clinical Data; Clinical Medicine; Comparative Effectiveness Research; Complement; Complex; Consensus; Data; Data Aggregation; Data Collection; Decentralization; Development; Disease; Distributed Databases; Electronic Health Record; Ethics; Faculty; Failure; Fibrinogen; Funding; Genomic medicine; Genomics; Goals; Health Care Research; Healthcare; Hybrids; Infrastructure; Institution; Institutional Policy; Intuition; Investigation; Knowledge; Libraries; Machine Learning; Mainstreaming; Maintenance; Medicine; Metadata; Methods; Mission; Modeling; Monitor; National Human Genome Research Institute; Outcome; Pathway interactions; Patient Care; Patients; Population; Positioning Attribute; Predisposition; Privacy; Privatization; Process; Protocols documentation; Records; Research; Research Infrastructure; Research Personnel; Risk; Secure; Security; Site; Standardization; System; Techniques; Technology; Testing; Therapeutic Agents; Time; Training; Transact; United States National Institutes of Health; Universities; Variant; base; biomedical informatics; blockchain; career; clinical care; clinical phenotype; clinically significant; computer science; data sharing; design; digital; diverse data; health care delivery; improved; innovation; interoperability; knowledge base; machine learning algorithm; machine learning method; medical specialties; network architecture; novel; open source; peer; peer networks; point of care; predictive modeling; privacy preservation; privacy protection; programs; public trust; structural genomics; success; trend; web portal; web services

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.

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 建模变得越来越重要，并有可能使可推广的模型能够 加速研究并促进质量改进计划。例如，了解是否 特定变量具有临床意义取决于多种因素，一个重要的因素是统计学上的一个重要因素 变体和临床表型之间的显着关联。预测的多元模型 接受某些治疗剂后，疾病或结局的易感性可以帮助推动基因组 药物为主流临床护理。但是，大多数现有的隐私机器学习方法 给定临床数据的已用于构建预测模型的，这些模型基于集中式体系结构， 提出安全性和鲁棒性漏洞，例如单点失败。 在此提案中，我们将开发出分散的保护隐私医学预测的新颖方法 建模，可以提高比较有效性研究，生物医学发现和患者护理。我们的 第一个目的是在私人区块链网络上开发一个预测建模框架。这个目标依赖于 区块链技术和共识协议，以及在线和批处理机器学习算法， 提供一个基于开源区块链的隐私预测建模库，以进一步 区块链相关的研究和应用。我们将表征区块链技术提供的设置 超过当前技术的进步。第二个目的是开发基于区块链的隐私保护 现实世界临床数据研究网络的基因组医学建筑建筑。这些目标是专门的 到国家人类基因组研究所（NHGRI）的使命开发生物医学技术 具有基因组学和医疗保健的应用领域。 NIH独立奖奖为申请人提供了一个很好的机会 具有生物医学知识的计算机科学背景，以及机器学习和 基于知识的系统。这也将使他能够调查新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的长期职业目标 在美国一所主要研究大学的生物医学信息学计划上的教师职位并进行 独立资助的在分散的隐私计算领域的研究。