Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.
Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.
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DOI：
10.2196/medinform.7744
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发表时间：
2018-04-13
影响因子：
3.2
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Lee J;Sun J;Wang F;Wang S;Jun CH;Jiang X
迫切需要开发全球分析框架，以便在跨多个机构的隐私保护联合环境中执行分析，而不会泄露隐私。最近进行了一些关于联合医学分析主题的研究，重点是几种算法。然而，它们都没有解决类似的患者匹配问题，这对于跨机构观察研究的队列构建、疾病监测和临床试验招募等应用很有用。本研究的目的是在联合环境中提供一个隐私保护平台，用于跨机构的患者相似性学习。在不共享患者级别信息的情况下，我们的模型可以从一家医院找到另一家医院的类似患者。我们提出了一个联合患者哈希框架，并开发了一种新颖的算法来学习特定于上下文的哈希代码来代表跨机构的患者。可以使用相应患者的结果哈希码有效地计算患者之间的相似性。为了避免对模型进行逆向工程的安全攻击，我们将同态加密应用于联合设置中的患者相似性搜索。我们使用从重症监护多参数智能监测-III数据库中提取的连续医疗事件来评估所提出的算法独立预测五种疾病发病率的能力。我们的算法在 κ=3 的 κ 最近邻中，平衡和不平衡数据的曲线下平均面积分别为 0.9154 和 0.8012。我们还通过使用同态加密确认了相似性搜索中的隐私保护。所提出的算法可以帮助有效地搜索跨机构的相似患者，以保护隐私的方式支持联合数据分析。
There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment. The aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patients from one hospital to another. We proposed a federated patient hashing framework and developed a novel algorithm to learn context-specific hash codes to represent patients across institutions. The similarities between patients can be efficiently computed using the resulting hash codes of corresponding patients. To avoid security attack from reverse engineering on the model, we applied homomorphic encryption to patient similarity search in a federated setting. We used sequential medical events extracted from the Multiparameter Intelligent Monitoring in Intensive Care-III database to evaluate the proposed algorithm in predicting the incidence of five diseases independently. Our algorithm achieved averaged area under the curves of 0.9154 and 0.8012 with balanced and imbalanced data, respectively, in κ-nearest neighbor with κ=3. We also confirmed privacy preservation in similarity search by using homomorphic encryption. The proposed algorithm can help search similar patients across institutions effectively to support federated data analysis in a privacy-preserving manner.
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影响因子：
3.7
作者：
Gong Y;Li Y;Abdolmaleky HM;Li L;Zhou JR
通讯作者：
Zhou JR
影响因子：
46.9
作者：
通讯作者：
--
DOI：
10.1136/amiajnl-2012-000896
发表时间：
2013-06-01
影响因子：
6.4
作者：
Newton, Katherine M.;Peissig, Peggy L.;Denny, Joshua C.
通讯作者：
Denny, Joshua C.
影响因子：
2.1
作者：
Gallego, Blanca;Walter, Scott R.;Coiera, Enrico
通讯作者：
Coiera, Enrico
影响因子：
37.8
作者：
Goldberger, AL;Amaral, LAN;Stanley, HE
通讯作者：
Stanley, HE