Privacy-preserving methods and tools for handling missing data in distributed health data networks

用于处理分布式健康数据网络中丢失数据的隐私保护方法和工具

• 批准号: 9364071
• 负责人: Qi Long
• 金额: $ 59.85万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-08 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9364071
• 关键词: Address; Algorithms; Area; Client; Clinical Data; Clinical Research; Collaborations; Communication; Computer software; Data; Databases; Disclosure; Docking; Electronic Health Record; Environment; Goals; Health; Healthcare Systems; Hospitals; Individual; Informatics; Institution; Institutional Policy; Insurance; Learning; Legal; Methodology; Methods; Obesity; Patients; Pattern; Privacy; Privatization; Probability; Provider; Research; Research Personnel; Secure; Security; Site; System; Weight; biomedical informatics; cost; data sharing; distributed data; electronic data; health care service; health data; improved; interest; open source; patient privacy; precision medicine; profiles in patients; public trust; repository; scale up; simulation; statistics; tool; user-friendly; web interface

PROJECT SUMMARY Distributed health data networks (DHDNs) that leverage electronic health records (EHRs) (e.g., eMerge, pSCANNER, PEDSnet) have drawn substantial interests in recent years, as they a) eliminate the need to create, maintain, and secure access to central data repositories, b) minimize the need to disclose protected health information outside the data-owning entity, and c) mitigate many security, proprietary, legal, and privacy concerns. Missing data are ubiquitous and present analytical challenges in DHDNs. However, very limited research has been conducted to address missing data in such settings. When applying to a distributed environment, the current state-of-the-art approaches for handling missing data require pooling raw data into a central repository before analysis and hence require individual-level data sharing, which may not be feasible for a number of reasons, including institutional policies prohibiting such sharing, high regulatory hurdles, public privacy concerns, and costs/overhead of moving massive amounts of data. A large body of research has demonstrated that given some background information about an individual such as data from EHRs, an adversary can learn (from “de-identified” data) sensitive information about the individual and improper disclosure of individual-level data may have serious implications. The proposed research will address the challenges associated with handling missing data in distributed analysis and fill a crucial methodology gap. We propose the following specific aims: 1) develop privacy-preserving distributed methods for handling missing data in horizontally partitioned data; 2) develop privacy preserving distributed methods for handling missing data in vertically partitioned data; 3) develop a user-friendly toolkit to allow researchers to handle missing data for distributed analysis in health data networks; and 4) evaluate and validate the methods and tool kit using the UCSD obesity patient data prepared for pSCANNER, and data from PEDSnet in addition to simulated data. The proposed approaches will enable using data across multiple sites and will not require pooling patient-level data into a central repository. They can be scaled up to handle massive amounts of data in DHDNs, because the decomposed computation can be parallelized to all participating parties. The results of our study will significantly advance the state-of-the-art in missing data methodology for DHDNs. The privacy-preserving software toolkit will enable researchers to use more complete data in their research by leveraging information from multiple sites without compromising patient privacy, and help lower regulatory and other hurdles for collaboration across multiple institutions and build the public trust. As such, it will encourage more institutions and healthcare systems to become part of a clinical data research network and more patients to participate in clinical studies, which will improve the validity, robustness and generalizability of research findings and offer substantial benefits in areas including, but not limited to, precision medicine and informatics practice.

项目摘要 利用电子健康记录（EHRS）的分布式健康数据网络（DHDN）（例如Emerge， PSCANNER，PEDSNET）近年来引起了重大利益，因为它们a）消除了需要 创建，维护并确保访问中央数据存储库，b）最大程度地减少披露受保护的需求 数据拥有实体之外的健康信息，c）减轻许多安全性，专有，法律和隐私 关注。丢失的数据无处不在，并且在DHDN中存在分析挑战。但是，非常有限 已经进行了研究以解决此类设置中缺少的数据。申请分布式 环境，当前处理丢失数据的最新方法需要将原始数据汇总到一个 中央存储库在分析之前，因此需要个人级数据共享，这可能是不可行的 由于多种原因，包括机构政策禁止这种共享，高监管障碍，公众 隐私问题以及移动大量数据的成本/开销。大量研究有 证明了有关个人的一些背景信息，例如来自EHR的数据， 对手可以（从“取消识别”数据）学习有关个人和不当的敏感信息 个人级别数据的披露可能具有严重的影响。拟议的研究将解决 与处理分布式分析中缺失的数据相关的挑战并填补了关键的方法论差距。我们 建议以下具体目的：1）开发保存隐私的分布式方法来处理丢失 水平分区数据中的数据； 2）开发隐私保留分布式方法以处理缺失 垂直分区数据中的数据； 3）开发一个用户友好的工具包，以允许研究人员处理丢失的数据 用于健康数据网络中的分布式分析； 4）使用该方法和工具套件评估和验证工具套件 UCSD肥胖患者数据为PSCANNER制备了，除了模拟数据外，还来自PEDSNET的数据。 提出的方法将使用跨多个站点的数据启用，并且不需要汇总患者级别 将数据数据放入中央存储库中。可以将它们缩放以处理DHDN中的大量数据，因为 分解计算可以与所有参与方并行。我们的研究结果将 在DHDN的缺少数据方法中，显着提高了最新技术。隐私保护 软件工具包将通过利用信息来使研究人员在研究中使用更多完整的数据 来自没有损害患者隐私的多个站点，并帮助降低监管和其他障碍 跨多个机构的合作并建立公众信任。因此，它将鼓励更多的机构 和医疗保健系统成为临床数据研究网络的一部分，并参与更多患者 临床研究将提高研究结果的有效性，鲁棒性和普遍性 在包括但不限于精密医学和信息实践在内的领域的重大好处。