Anonymization of longitudinal electronic medical records.
Anonymization of longitudinal electronic medical records.
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DOI：
10.1109/titb.2012.2185850
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发表时间：
2012-05
期刊：
影响因子：
--
通讯作者：
Malin B
中科院分区：
文献类型：
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作者：
Tamersoy A;Loukides G;Nergiz ME;Saygin Y;Malin B
电子病历（EMR）系统已经使得医疗保健提供者能够从初级保健领域收集详细的患者信息。与此同时，来自EMR的纵向数据越来越多地与生物储存库相结合，以生成个性化的临床决策支持方案。新兴的政策鼓励研究人员以去识别的形式传播这些数据，以供重用和协作，但组织对此犹豫不决，因为他们担心这样的行为会危及患者隐私。特别是，有人担心，残留的人口统计学和临床特征可能被用于重新识别目的。已经开发了各种方法来匿名化临床数据，但它们忽略了时间信息，因此不足以用于新兴的生物医学研究范式。本文提出了一种新的方法来共享患者特定的纵向数据，提供强大的隐私保证，同时保留许多生物医学研究的数据效用。我们的方法聚合的时间和诊断信息，使用启发序列比对和聚类方法。我们证明，所提出的方法可以生成匿名数据，允许有效的生物医学分析，使用来自范德比尔特大学医学中心的EMR系统的几个患者队列。
Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center.