Duan Rui, Boland Mary Regina, Moore Jason H, Chen Yong
Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, 423 Guardian Drive, PA, 19104, USA#Co-first author.
Pac Symp Biocomput. 2019;24:30-41.
Electronic Health Records (EHR) contain extensive information on various health outcomes and risk factors, and therefore have been broadly used in healthcare research. Integrating EHR data from multiple clinical sites can accelerate knowledge discovery and risk prediction by providing a larger sample size in a more general population which potentially reduces clinical bias and improves estimation and prediction accuracy. To overcome the barrier of patient-level data sharing, distributed algorithms are developed to conduct statistical analyses across multiple sites through sharing only aggregated information. The current distributed algorithm often requires iterative information evaluation and transferring across sites, which can potentially lead to a high communication cost in practical settings. In this study, we propose a privacy-preserving and communication-efficient distributed algorithm for logistic regression without requiring iterative communications across sites. Our simulation study showed our algorithm reached comparative accuracy comparing to the oracle estimator where data are pooled together. We applied our algorithm to an EHR data from the University of Pennsylvania health system to evaluate the risks of fetal loss due to various medication exposures.
电子健康记录（EHR）包含有关各种健康结果和风险因素的广泛信息，因此已在医疗保健研究中广泛使用。整合来自多个临床站点的EHR数据可以通过在更广泛的人群中提供更大的样本量来加速知识发现和风险预测，这有可能减少临床偏差并提高估计和预测准确性。为了克服患者层面数据共享的障碍，人们开发了分布式算法，通过仅共享汇总信息来跨多个站点进行统计分析。当前的分布式算法通常需要在站点之间进行迭代信息评估和传输，这在实际应用中可能会导致高昂的通信成本。在本研究中，我们提出了一种用于逻辑回归的隐私保护且通信高效的分布式算法，该算法无需在站点之间进行迭代通信。我们的模拟研究表明，与将数据集中在一起的神谕估计器相比，我们的算法达到了相当的准确性。我们将我们的算法应用于宾夕法尼亚大学医疗系统的EHR数据，以评估各种药物暴露导致胎儿丢失的风险。
