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Pseudonymization of Radiology Data for Research Purposes

机译：用于研究目的的放射学数据的假名化

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Medical image processing methods and algorithms, developed by researchers, need to be validated and tested. Test data would ideally be real clinical data especially that clinical data is varied and exists in large volumes. Nowadays, clinical data is accessible electronically and has important value for researchers. However, the usage of clinical data for research purposes should respect data confidentiality, patient right to privacy, and patient consent. In fact, clinical data is nominative given that it contains information about the patient such as name, age, and identification number. Evidently, clinical data needs to be de-identified to be exported to research databases. However, the same patient is usually followed during a long period of time. The disease progression and the diagnostic evolution represent extremely valuable information for researchers as well. Our objective is to build a research database from de-identified clinical data while enabling the data set to be easily incremented by exporting new pseudonymous data, acquired over a long period of time. Pseudonymization is data de-identification, such that data belonging to an individual in the clinical environment still belong to the same individual in the de-identified research version. In this paper, we explore various software architectures to enable the implementation of an imaging research database that can be incremented in time. We also evaluate their security and discuss their security pitfalls. As most imaging data accessible electronically is available with the digital imaging and communication in medicine (DICOM) standard, we propose a de-identification scheme that closely follows DICOM recommendations. Our work can be used to enable electronic health record (EHR) secondary usage such as public surveillance and research, while maintaining patient confidentiality.
机译：研究人员开发的医学图像处理方法和算法需要进行验证和测试。理想地，测试数据将是真实的临床数据，尤其是临床数据是变化的并且大量存在。如今，临床数据可通过电子方式获取，对研究人员具有重要价值。但是，将临床数据用于研究目的应尊重数据的保密性，患者的隐私权和患者的同意。实际上，临床数据是重要的，因为它包含有关患者的信息，例如姓名，年龄和身份证号码。显然，临床数据需要取消标识才能导出到研究数据库。但是，通常会长时间跟踪同一名患者。疾病进展和诊断演变也为研究人员提供了极为宝贵的信息。我们的目标是从身份不明的临床数据构建研究数据库，同时通过导出长时间获取的新假名数据，轻松地增加数据集。假名化是数据去识别，因此在临床环境中属于个人的数据在去识别的研究版本中仍属于同一个人。在本文中，我们探索了各种软件体系结构，以实现可以随时间增加的影像研究数据库的实现。我们还评估了它们的安全性并讨论了它们的安全隐患。由于大多数可通过电子方式获取的成像数据都可以通过医学数字成像和通信（DICOM）标准获得，因此我们提出了一种与DICOM建议密切相关的去识别方案。我们的工作可用于实现电子健康记录（EHR）的二次使用，例如公共监视和研究，同时保持患者的机密性。

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