Privacy technology to support data sharing for comparative effectiveness research: a systematic review.
Privacy technology to support data sharing for comparative effectiveness research: a systematic review.
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DOI：
10.1097/mlr.0b013e31829b1d10
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发表时间：
2013-08
期刊：
影响因子：
3
通讯作者：
Ohno-Machado L
中科院分区：
文献类型：
--
作者：
Jiang X;Sarwate AD;Ohno-Machado L
有效的数据共享对于比较有效性研究 (CER) 至关重要，但人们对患者数据的不当披露存在重大担忧。这些担忧刺激了隐私保护数据共享和数据挖掘新技术的发展。我们的目标是审查可能适合 CER 相关数据共享的现有和新兴技术。我们采用系统综述方法来全面检索研究文献。我们检索了 7 个数据库，并根据标题、摘要和全文进行了三个阶段的过滤，以识别与 CER 最相关的作品。基于协议并利用第三方专家的套利，我们选择了 97 篇文章进行荟萃分析。我们的研究结果按照 CER 应用程序中数据共享的主要类型（即机构间、机构托管和公开发布）进行组织。我们根据具体场景提出了建议。我们将研究范围限制在能够证明实际影响的方法上，从而消除了其他地方调查过的许多隐私理论研究。我们进一步将我们的研究限制为数据表的数据共享，而不是复杂的基因组、集值、时间序列、文本、图像或网络数据。最先进的隐私保护技术可以指导实用工具的开发，从而扩大未来的 CER 研究。然而，在实际评估以及更广泛的数据类型的应用方面，这个快速发展的领域仍然存在许多挑战。
Effective data sharing is critical for comparative effectiveness research (CER), but there are significant concerns about inappropriate disclosure of patient data. These concerns have spurred the development of new technologies for privacy preserving data sharing and data mining. Our goal is to review existing and emerging techniques that may be appropriate for data sharing related to CER. We adapted a systematic review methodology to comprehensively search the research literature. We searched 7 databases and applied three stages of filtering based on titles, abstracts, and full text to identify those works most relevant to CER. Based on agreement and using the arbitrage of a third party expert, we selected 97 articles for meta-analysis. Our findings are organized along major types of data sharing in CER applications (i.e., institution-to-institution, institution-hosted, and public release). We made recommendations based on specific scenarios. We limited the scope of our study to methods that demonstrated practical impact, eliminating many theoretical studies of privacy that have been surveyed elsewhere. We further limited our study to data sharing for data tables, rather than complex genomic, set-valued, time series, text, image, or network data. State-of-the-art privacy preserving technologies can guide the development of practical tools that will scale up the CER studies of the future. However, many challenges remain in this fast moving field in terms of practical evaluations as well as applications to a wider range of data types.