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: 10709531
• 负责人: Tsung-Ting Kuo
• 金额: $ 32.72万
• 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/10709531
• 关键词: Acceleration; 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; Health protection; 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; Process; Protocols documentation; Records; Regulation; Research Personnel; Risk; Security; Site; Societies; System; Technology; Testing; Training; United States National Institutes of Health; Universities; Validation; Work; 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; privacy protection; 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主要挑战之一， 特别是用于医疗保健研究。重新确定风险和数据泄露需要新的政策和法规 用于跨医疗机构和研究实验室的数据共享。虽然政策无法解决问题 就其本身而言，与政策携手合作的高级技术对于解决该政策很重要 隐私/安全问题。预测分析可以支持质量提高，临床研究，并最终支持 影响患者的健康状况。来自多个的大量临床变量信息和大量数据记录 机构和实验室对于进一步提高建模方法的性能和实验室是必要的 确定疾病的药物结果关联。但是，这种敏感数据在 机构/实验室可能会出现严重的隐私风险，这可能会危害NIH的使命。瞄准 通过跨机构建模，可以减轻隐私问题，同时提高预测能力，先前的研究 提出的分布式方法仅交换预测模型，但不交换患者数据。但是，这些 方法仍然对临床跨机构学习问题构成许多挑战，包括需要 更全面的临床变量和更多的患者记录，以实现更好的预测歧视和 建立更具概括的模型，这是发现/减轻数据操纵以增加的必要的模型 经过协作培训的模型的可信赖性，以及对确保可用性的更多验证的要求。 在此提案中，我们计划开发SOCAL（保护跨实验室的临床数据的隐私共享）， 分布式框架通过将垂直/水平建模方法集成到 包括更多完整的变量和更多记录，发现/减轻数据操纵事件 使用记录在区块链上的模型，并进行受控实验并设计/测试Web门户网站 与物理研究者一起提高系统的可用性。 SOCAL将在冠状病毒上进行评估 来自加利福尼亚大学（UC）卫生医疗中心的疾病2019（COVID-19）数据集。我们期望 协作建模的知识/能力可以提高，学习过程的可信度可以 增强，框架将准备好使用。 SOCAL具有创新性，因为它将是一个新的整合 垂直/水平建模的方法，一种新型的数据操纵方法和硬化的方法 实用区块链应用的原型。我们预计SOCAL框架对 在很大程度上减少了包括医疗保健在内的各种利益相关者的预测建模任务的隐私问题 提供者，临床研究人员和患者。完成后，SOCAL可以加速 方法/技术增加了机构参与这种协作以改进的意愿 医疗保健的有效性。