A novel platform for synthetic generation and statistical obfuscation of tabular clinical data, simulated images, and machine-generated text

用于表格临床数据、模拟图像和机器生成文本的合成生成和统计混淆的新颖平台

• 批准号: 10696488
• 负责人: Ronak Shetty
• 金额: $ 32.46万
• 依托单位: GRAYRAIN, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10696488
• 关键词: Address; Algorithms; Automobile Driving; Back; Behavior; Biomedical Research; Businesses; Clinical; Clinical Data; Complement; Complex; Data; Data Protection; Data Set; Disclosure; Education; Equilibrium; Fast Healthcare Interoperability Resources; Generations; Goals; Hares; Health; Health Care Research; Health Care Sector; Health Insurance Portability and Accountability Act; Healthcare; Image; Industry; Infrastructure; Institution; Legal patent; Magnetic Resonance Imaging; Masks; Medical; Modeling; Nurses; Occupations; Patient Care; Phase; Positron-Emission Tomography; Privacy; Process; Production; Protocols documentation; Records; Regulation; Reporting; Research; Risk; Secure; Services; Small Business Technology Transfer Research; Societies; Socioeconomic Status; Statutes and Laws; Structure; Sum; Techniques; Technology; Testing; Text; Time; big biomedical data; clinical imaging; cost effective; data anonymization; data de-identification; data exchange; data format; data repository; data sharing; desensitization; design; electronic health data; electronic structure; flexibility; innovation; interest; interoperability; non-compliance; novel; predictive modeling; privacy protection; software as a service; structured data; tool; unstructured data; web app; web services

PROJECT SUMMARY Data is a critical and highly valuable commodity, driving meaningful change in our society, especially when it pertains to patient care and biomedical research. Currently, institutions pay inordinate sums to increase, regain, and complement their data panels. As an extra burden, data legislation and privacy protection regulations introduce barriers to forming effective partnerships between business, clinical, research and educational organizations. As a result, approximately 80% of medical data today can’t be readily shared because they contain personal, protected or sensitive information and remains unstructured and untapped after they are created. There is a growing and urgent unmet need for technology solutions that balance research and commercial organizations interests by supporting flexible general-purpose analytics while guaranteeing privacy protection. There are no effective mechanisms to enable data sharing without either risking inappropriate release of sensitive information or potential degradation of the information content. The currently available few protocols and algorithms for modeling, processing, interrogating, and ultimately sharing large sensitive data (e.g., thousands and millions of records with thousands of heterogeneous features) all share significant limitations and their practical use still lags behind research progress. Two major unmet needs in the data sharing industry are i) the inability to return de-identified clones of the raw data, and ii) lack of scalability requirements of production deployments. GrayRain, LLC is an early-stage Software-as-a-Service company developing a novel platform for statistical obfuscation and de- identification of sensitive structured (numerical, categorical tabular data) and unstructured information (e.g., clinical text, doctors/nurses notes and clinical images, such as MRI, PET). The core of GrayRain’s technology is the novel patented statistical obfuscation algorithm, DataSifter. The technology proposed in this STTR Phase I application will significantly increase the number of secure data transactions in the healthcare sector and beyond, enabling data sharing with fully controllable risk of identification of any sensitive information, including, but not limited to PHI (personal health information), demographic information, or socioeconomic status. GrayRain’s technology is able to produce de-identified clones of raw tabular data, addressing a major limitations encounter across existing data anonymization protocols. As far as scalability, the main goal of this STTR Phase I is to establish feasibility of GrayRain to accurately and efficiently (re: scalability) de- identify and share large-scale complex EHR data repositories with a controlled risk of disclosing protected or personal health information .

项目概要 数据是一种至关重要且极具价值的商品，推动着我们社会的有意义的变革， 特别是当涉及患者护理和生物医学研究时，目前由机构付费。 增加、恢复和补充他们的数据面板的金额过多，作为额外的负担， 数据立法和隐私保护出台法规形成有效壁垒 因此，商业、临床、研究和教育组织之间的伙伴关系。 如今大约 80% 的医疗数据无法轻易共享，因为它们包含 个人的、受保护的或敏感的信息，并且在这些信息之后仍然是非结构化的和未被利用的 对平衡技术解决方案的需求日益增长且未得到满足。 通过支持灵活的通用目的来满足研究和商业组织的利益 分析的同时保证隐私保护。 没有有效的机制来实现 数据共享，不会带来敏感信息不当发布的风险或潜在的风险 目前可用的协议和算法很少。 建模、处理、询问并最终共享大量敏感数据（例如数千个 以及具有数千个异构特征的数百万条记录）都共享重要的 局限性及其实际应用仍然落后于研究进展的两个主要未满足的需求。 数据共享行业的特点是 i) 无法返回原始数据的去识别化克隆，以及 ii) GrayRain, LLC 处于早期阶段，缺乏可扩展性要求。 软件即服务公司开发用于统计混淆和反演的新颖平台 识别敏感的结构化（数字、分类表格数据）和非结构化数据 信息（例如临床文本、医生/护士笔记和临床图像，例如 MRI、PET）。 GrayRain 技术的核心是新颖的专利统计混淆算法 DataSifter。 STTR 第一阶段申请中提出的技术将显着增加 确保医疗保健行业及其他领域的数据交易安全，从而实现数据共享 识别任何敏感信息（包括但不限于 PHI）的可控风险 （个人健康信息）、人口统计信息或社会经济状况。 技术能够生成原始表格数据的去识别化克隆，解决了主要限制 就可扩展性而言，这是其主要目标。 STTR 第一阶段是建立 GrayRain 准确有效（可扩展性）de-的可行性 识别和共享大规模复杂的 EHR 数据存储库，并控制泄露风险 受保护的或个人的健康信息。