SOCAL: Privacy-protecting Sharing Of Clinical Data Across Laboratories

SOCAL：跨实验室临床数据的隐私保护共享

• 批准号: 10522949
• 负责人: Tsung-Ting Kuo
• 金额: $ 32.84万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522949
• 关键词: Address; Artificial Intelligence; COVID-19; California; Clinical; Clinical Data; Clinical Research; Collaborations; Data; Data Set; Decentralization; Discrimination; Disease; Effectiveness; Electronic Health Record; Ensure; Extravasation; Failure; Health; Health Personnel; Health Status; Healthcare; Hospitals; Institution; Intuition; Knowledge; Laboratories; Laboratory Research; Learning; Machine Learning; Medical center; Methodology; Methods; Mission; Modeling; Modernization; Outcome; Patients; Performance; Pharmaceutical Preparations; Physicians; Policies; Predictive Analytics; Primary Care Physician; Privacy; Problem Solving; Process; Protocols documentation; Records; Regulation; Research Personnel; Risk; Security; Site; Societies; System; Technology; Testing; Training; United States National Institutes of Health; Universities; Validation; Work; base; blockchain; care providers; data sharing; data warehouse; design; experimental study; health care quality; improved; innovation; learning strategy; method development; novel; peer; predictive modeling; privacy preservation; prototype; trustworthiness; usability; web portal; willingness

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.

项目摘要 个人信息的隐私和安全已成为现代社会的重大挑战之一， 尤其是在医疗研究中。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，与政策携手合作的先进技术对于解决 隐私/安全问题。预测分析可以支持质量改进、临床研究， 影响患者健康状况。广泛的临床变量信息和来自多个 机构和实验室是必要的，以进一步提高性能的建模方法， 确定药物治疗与疾病结果之间的关联。然而，这些敏感数据在 机构/实验室可能存在严重的隐私风险，这可能危及NIH的使命。针对 缓解隐私问题，同时通过跨机构建模提高预测能力， 提出了分布式方法，仅交换预测模型，而不交换患者数据。但这些 方法仍然对临床跨机构学习问题提出了许多挑战，包括需要 更全面的临床变量和更多的患者记录，以实现更好的预测区分， 建立更通用的模型，发现/减轻数据操作的必要性，以增加 协作训练模型的可信度，以及需要更多验证以确保可用性。 在本提案中，我们计划开发SOCAL（隐私保护跨实验室临床数据共享）， 分布式框架通过集成垂直/水平建模方法来解决这些挑战， 包括更完整变量和更多记录，发现/减轻数据操纵事件 使用记录在区块链上的模型，进行受控实验并设计/测试门户网站 与医生研究人员合作，以提高系统的可用性。SOCAL将在冠状病毒上进行评估 来自五个加州大学（UC）健康医疗中心的2019年疾病（COVID-19）数据集。我们预计 知识/能力的协作建模可以提高，学习过程的可信度可以 该框架将得到加强，并准备投入使用。SOCAL是创新的，因为它将是一个新的整合 垂直/水平建模的方法，一种新的数据操纵抵抗方法，以及一种硬化的 实际区块链应用的原型。我们预计SOCAL框架的强大影响， 大大减少了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担忧 提供者、临床研究人员和患者。完成后，SOCAL可以加快发展， 方法/技术，以提高机构参与这种合作的意愿， 医疗保健的有效性。