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.

翻译：目的：本文针对具有结构性与偶发性缺失特征的电子健康记录（EHRs）提出一种新型插补方法。此类缺失模式在整合异构EHR数据集以支持下游临床应用时频繁出现。通过填补这些数据缺口，本方法为集成分析提供了实用解决方案，从而提升数据效用并深化对群体健康的理解。材料与方法：我们首先阐明EHR数据集成分析中的结构性及偶发性缺失机制。随后，我们提出一种专门针对结构性与异质性缺失数据设计的新型插补框架——Macomss。我们为Macomss建立了理论保证，确保其在维护集成分析完整性与可靠性方面的鲁棒性。为评估其实际性能，我们开展了广泛的模拟研究，复现了真实世界EHR系统中观察到的复杂缺失模式，并辅以杜克大学健康系统（DUHS）的EHR数据集进行验证。结果：模拟研究表明，本方法在各项指标上持续优于现有插补方法。基于DUHS内三家医院的数据集，Macomss在多数情况下实现了最低的缺失数据插补误差，且与基准方法相比，其下游预测性能表现更优或相当。结论：我们提出了一种具有理论保证且实践意义显著的方法，用于处理结构性与偶发性缺失数据，从而支持跨多源EHR数据集的精准可靠集成分析。该方法对推进群体健康研究具有重要潜力。