SOCAL: Privacy-protecting Sharing Of Clinical Data Across Laboratories

项目摘要 个人信息的隐私和安全已经成为现代社会的重大挑战之一， 尤其是在医疗保健研究方面。重新识别风险和数据泄露需要新的政策和法规 用于医疗机构和研究实验室之间的数据共享。虽然政策不能解决问题 就其本身而言，与政策携手工作的先进技术对于解决 隐私/安全问题。预测性分析可以支持质量改进、临床研究，并最终 影响患者的健康状况。大量的临床变量信息和海量的数据记录 机构和实验室是必要的，以进一步改进建模方法的性能，并 确定用药结果与疾病的关系。尽管如此，这些敏感数据在 机构/实验室可能带来严重的隐私风险，这可能危及NIH的使命。瞄准 通过跨机构建模缓解隐私问题，同时提高预测能力，先前的研究 建议的分布式方法只交换预测模型，而不交换患者数据。然而，这些 方法仍然对临床跨机构学习问题提出许多挑战，包括需要 更全面的临床变量和更多的患者记录，以实现更好的预测和区分 构建更具通用性的模型，发现/缓解数据处理的必要性，以增加 协作训练的模型的可信性，以及对更多验证以确保可用性的要求。 在这项提案中，我们计划开发SoCal(跨实验室的临床数据隐私保护共享)，a 分布式框架通过将垂直/水平建模方法集成到 包括更完整变量和更多记录，从而发现/缓解数据操作事件 使用区块链上记录的模型，进行受控实验，设计/测试门户网站 与医生-研究人员一起增加系统的可用性。SoCal将在一种冠状病毒上进行评估 来自加州大学(UC)五个健康医疗中心的2019年疾病(新冠肺炎)数据集。我们期待着 可以提高协同建模的知识/能力，学习过程的可信度可以 如果得到加强，框架就可以使用了。SoCal是创新的，因为它将是一种新的整合 垂直/水平建模方法，一种新的数据操作抵抗方法，以及一种强化的 一个实际区块链应用的原型。我们预计SoCal框架将产生强大的影响 大大减少包括医疗保健在内的各种利益相关者对预测建模任务的隐私顾虑 提供者、临床研究人员和患者。建成后，SoCal可以加快 提高各机构参与这种协作以改进的意愿的方法/技术 医疗保健的有效性。

Project Summary Privacy and security of personal information has become one of the major grand challenges in modern society, especially for healthcare studies. Re-identification risks and data breaches require new policies and regulations for data sharing across healthcare institutions and research laboratories. While policy cannot solve the problem on its own, advanced technologies that work hand in hand with policy are important to address the privacy/security concerns. Predictive analytics can support quality improvement, clinical research, and eventually impact patient health status. Extensive clinical variable information and voluminous data records from multiple institutions and laboratories are necessary to further improve the performance of modeling approaches and to identify medication-outcome associations for diseases. Nonetheless, the transfer of such sensitive data among institutions/laboratories can present serious privacy risks, which can jeopardize NIH’s mission. Aiming at mitigating the privacy problem while increasing predictive capability via cross-institutional modeling, prior studies proposed distributed methods to exchange only the predictive models, but not patient data. However, these methods still pose many challenges to the clinical cross-institutional learning problem, including the need for more comprehensive clinical variables and more patient records to achieve better prediction discrimination and build more generalizable models, the necessity for discovery/alleviation of data manipulation to increase the trustworthiness of the collaboratively trained models, and the requirement for more validation to ensure usability. In this proposal, we plan to develop SOCAL (Privacy-protecting Sharing Of Clinical data Across Laboratories), a distributed framework addressing these challenges by integrating vertical/horizontal modeling methods to include both more complete variables and more records, discovering/alleviating data manipulation incidents using models recorded on blockchain, and conducting controlled experiments and designing/testing a web portal with physician-researchers to increase the usability of the system. SOCAL will be evaluated on a Coronavirus Disease 2019 (COVID-19) dataset from five University of California (UC) Health medical centers. We expect the knowledge/capability of collaborative modeling can be improved, the trustworthiness of the learning process can be enhanced, and the framework will be ready for use. SOCAL is innovative because it will be a new integration methodology for vertical/horizontal modeling, a novel data manipulation resisting methods, and a hardened prototype for a practical blockchain application. We anticipate a powerful impact of the SOCAL framework to largely reduce the privacy concerns of predictive modeling tasks for various stakeholders, including healthcare providers, clinical researchers, and patients. Upon completion, SOCAL can accelerate the development of methods/technologies to increase willingness of institutions to participate in such a collaboration for improving the effectiveness of healthcare.