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

项目总结 利用电子健康记录(EHR)的分布式健康数据网络(DHDN)(例如，Emerge， PSCANNER、PEDSnet)近年来引起了极大的兴趣，因为它们a)消除了 创建、维护和保护对中央数据存储库的访问，b)最大限度地减少披露受保护数据的需要 数据拥有实体之外的健康信息，以及c)减轻许多安全、专有、法律和隐私 担忧。数据缺失在数字人类发展网络中无处不在，给分析带来了挑战。然而，非常有限的 已经进行了研究，以解决在这种情况下丢失的数据。当应用于分布式 环境中，当前最先进的处理丢失数据的方法需要将原始数据汇集到 分析之前的中央存储库，因此需要个人级别的数据共享，这可能是不可行的 原因有很多，包括禁止这种分享的体制政策、高度的监管障碍、公众 隐私问题，以及移动海量数据的成本/开销。一大批研究表明 演示了给定一些关于个人的背景信息，例如来自EHR的数据， 敌手可以(从未识别的数据中)获知有关个人的敏感信息和不正当信息 披露个人层面的数据可能会产生严重影响。拟议的研究将解决 与处理分布式分析中的缺失数据相关的挑战，并填补了一个关键的方法空白。我们 提出以下具体目标：1)开发保护隐私的分布式失踪处理方法 水平分区数据中的数据；2)开发保护隐私的分布式方法来处理丢失 垂直分区数据中的数据；3)开发用户友好的工具包，使研究人员能够处理丢失的数据 用于卫生数据网络中的分布式分析；以及4)使用 加州大学圣迭戈分校为pSCANNER准备的肥胖患者数据，以及来自PEDSnet的数据和模拟数据。 建议的方法将支持跨多个站点使用数据，不需要共享患者级别的数据 将数据存入中央储存库。它们可以扩展以处理DHDN中的海量数据，因为 分解后的计算可以并行化到所有参与方。我们的研究结果将 大大提高了用于DHDN的缺失数据方法的最新水平。保护隐私 软件工具包将使研究人员能够通过利用信息在他们的研究中使用更完整的数据 在不损害患者隐私的情况下，帮助降低监管和其他方面的障碍 跨多个机构协作，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，并让更多的患者参与 临床研究，这将提高研究结果的有效性、稳健性和概括性，并提供 在包括但不限于精准医学和信息学实践的领域中带来实质性的好处。

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.