标题： 结构化和间歇性缺失的电子健康记录综合分析 显示英文标题

标题： Integrated Analysis for Electronic Health Records with Structured and Sporadic Missingness

摘要： 目标：我们提出了一种针对具有结构化和偶尔缺失性的电子健康记录（EHRs）的新型填补方法。这种缺失性在整合异构EHR数据集用于下游临床应用时经常出现。通过解决这些缺口，我们的方法为集成分析提供了实用的解决方案，提高了数据的实用性，并推动了对人群健康的理解。材料和方法：我们首先展示了在EHR数据分析集成中的结构化和偶尔缺失机制。随后，我们介绍了一个名为Macomss的新填补框架，专门设计用于处理结构化和异构发生的缺失数据。我们建立了Macomss的理论保证，确保其在保持集成分析的完整性和可靠性方面的稳健性。为了评估其经验性能，我们进行了广泛的模拟研究，复制了现实世界EHR系统中观察到的复杂缺失模式，并通过杜克大学健康系统（DUHS）的EHR数据集进行验证。结果：模拟研究表明，我们的方法在大多数情况下始终优于现有的填补方法。使用DUHS内三家医院的数据集，Macomss在大多数情况下实现了最低的填补误差，并且与基准方法相比，提供了更优或相当的下游预测性能。结论：我们提供了一种理论上保证且实际有意义的方法，用于填补结构化和偶尔缺失的数据，使多个EHR数据集的准确可靠集成分析成为可能。所提出的方法在推动人群健康研究方面具有重要意义。 显示英文摘要

摘要： Objectives: We propose a novel imputation method tailored for Electronic Health Records (EHRs) with structured and sporadic missingness. Such missingness frequently arises in the integration of heterogeneous EHR datasets for downstream clinical applications. By addressing these gaps, our method provides a practical solution for integrated analysis, enhancing data utility and advancing the understanding of population health. Materials and Methods: We begin by demonstrating structured and sporadic missing mechanisms in the integrated analysis of EHR data. Following this, we introduce a novel imputation framework, Macomss, specifically designed to handle structurally and heterogeneously occurring missing data. We establish theoretical guarantees for Macomss, ensuring its robustness in preserving the integrity and reliability of integrated analyses. To assess its empirical performance, we conduct extensive simulation studies that replicate the complex missingness patterns observed in real-world EHR systems, complemented by validation using EHR datasets from the Duke University Health System (DUHS). Results: Simulation studies show that our approach consistently outperforms existing imputation methods. Using datasets from three hospitals within DUHS, Macomss achieves the lowest imputation errors for missing data in most cases and provides superior or comparable downstream prediction performance compared to benchmark methods. Conclusions: We provide a theoretically guaranteed and practically meaningful method for imputing structured and sporadic missing data, enabling accurate and reliable integrated analysis across multiple EHR datasets. The proposed approach holds significant potential for advancing research in population health.