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 .

项目摘要 数据是一种至关重要且高度有价值的商品，推动了我们社会的有意义的变化， 特别是当它与患者护理和生物医学研究有关时。目前，机构付款 超额增加，继续并补充其数据面板。作为一个额外的伯恩， 数据立法和隐私保护法规引入了形成有效的障碍 业务，临床，研究和教育组织之间的伙伴关系。因此， 当今约有80％的医疗数据不容易共享，因为它们包含 个人，受保护或敏感的信息，并在他们之后保持无结构和未开发 被创建。对技术解决方案的需求不断增长且未满足的需求 研究和商业组织的兴趣通过支持灵活的通用物 分析同时保证隐私保护。 没有有效的机制可以启用 数据共享而不冒险释放敏感信息或潜在的可能性 信息内容的退化。当前可用的很少的协议和算法 建模，处理，询问和最终共享大型敏感数据（例如，成千上万 和数百万的记录具有数千个异质功能）都有重要的 局限性及其实际用途仍然落后于研究进度。两个主要未满足的需求 数据共享行业是i）无法返回原始数据的识别克隆，ii） 缺乏生产部署的可伸缩性要求。 Grayrain，LLC是一个早期 软件即服务公司开发一个新颖的平台，用于统计混淆和de- 识别敏感的结构化（数值，分类表数据）和非结构化 信息（例如，临床文本，医生/护士注意和临床图像，例如MRI，PET）。 Grayrain技术的核心是新颖的专利统计混淆算法，DataSifter。 在此STTR I期应用程序中提出的技术将大大增加 确保医疗保健领域及其他地区的数据交易，使数据共享可以完全 识别任何敏感信息的可控风险，包括但不限于PHI （个人健康信息），人口统计信息或社会经济状况。 Grayrain的 技术能够生成未识别的原始表格数据的克隆，从而解决了主要限制 在现有数据匿名协议中相遇。至于可伸缩性，主要目标 STTR I期是确定Grayrain的可行性，以准确有效地（RE：可伸缩性） 识别并共享具有披露风险的大规模复杂EHR数据存储库 受保护或个人健康信息。