Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.
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DOI：
10.1136/amiajnl-2012-000862
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发表时间：
2012-09
期刊：
影响因子：
--
通讯作者：
Ohno-Machado L
中科院分区：
文献类型：
--
作者：
Wu Y;Jiang X;Kim J;Ohno-Machado L
临床和基因组数据中复杂或罕见模式的分类需要大量标记的患者集。虽然对大型集中式数据源进行操作的方法已被广泛使用，但很少有人关注是否可以以分布式方式开发二元逻辑回归（LR）等模型，从而允许研究人员在不共享患者数据的情况下共享模型。我们不是将数据带到中央存储库进行计算，而是将计算带到数据中。网格二进制逻辑回归（GLORE）模型集成可分解的部分元素或非隐私敏感的预测值，以获得模型系数，方差-协方差矩阵，拟合优度检验统计量和受试者工作特征（ROC）曲线下面积。我们对模拟数据和临床相关数据进行了实验，并将GLORE的计算成本与使用组合数据估计的传统LR模型的计算成本进行了比较。我们证明了我们的结果与LR的结果相同，精度为10−15。此外，GLORE在计算上是高效的。在GLORE中，系数梯度的计算必须在不同的站点同步，这涉及到一些努力以确保通信的完整性。确保预测变量在数据集中具有相同的格式和含义是必要的。结果表明，GLORE的性能与LR一样好，并允许数据在其原始站点上保持保护。
The classification of complex or rare patterns in clinical and genomic data requires the availability of a large, labeled patient set. While methods that operate on large, centralized data sources have been extensively used, little attention has been paid to understanding whether models such as binary logistic regression (LR) can be developed in a distributed manner, allowing researchers to share models without necessarily sharing patient data. Instead of bringing data to a central repository for computation, we bring computation to the data. The Grid Binary LOgistic REgression (GLORE) model integrates decomposable partial elements or non-privacy sensitive prediction values to obtain model coefficients, the variance-covariance matrix, the goodness-of-fit test statistic, and the area under the receiver operating characteristic (ROC) curve. We conducted experiments on both simulated and clinically relevant data, and compared the computational costs of GLORE with those of a traditional LR model estimated using the combined data. We showed that our results are the same as those of LR to a 10−15 precision. In addition, GLORE is computationally efficient. In GLORE, the calculation of coefficient gradients must be synchronized at different sites, which involves some effort to ensure the integrity of communication. Ensuring that the predictors have the same format and meaning across the data sets is necessary. The results suggest that GLORE performs as well as LR and allows data to remain protected at their original sites.
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发表时间：
2012-03-01
影响因子：
6.4
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发表时间：
2011-05-01
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10.1136/amiajnl-2011-000751
发表时间：
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期刊：
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通讯作者：
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