Electronic health records (EHRs) have become a platform for data-driven granular-level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely, we consider a penalized multilevel generalized linear model. We decompose regional contribution into smooth and sparse signals, which are automatically identified by a combination of fusion and sparse penalties imposed on the likelihood function. In addition, we weigh the proposed likelihood to account for the missingness and potential nonrepresentativeness arising from the EHR data. We develop a novel alternating minimization algorithm, which is computationally efficient, easy to implement, and guarantees convergence. Simulation studies demonstrate superior performance of the proposed method. Finally, we apply our method to the University of Wisconsin Population Health Information Exchange database.

中文翻译：

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使用惩罚回归同时进行空间平滑和异常值检测，并应用于电子健康记录的儿童肥胖监测

近年来，电子健康记录 (EHR) 已成为数据驱动的细粒度监测平台。在本文中，我们利用 EHR 早期预防儿童肥胖。所提出的方法同时为肥胖流行提供了平滑的疾病映射和异常信息，这对于提高公众意识和促进有针对性的干预很有用。更准确地说，我们考虑了一个惩罚的多级广义线性模型。我们将区域贡献分解为平滑和稀疏的信号，这些信号通过对似然函数施加的融合和稀疏惩罚的组合自动识别。此外，我们权衡了提议的可能性，以解释 EHR 数据引起的缺失和潜在的非代表性。我们开发了一种新颖的交替最小化算法，计算效率高，易于实现，并保证收敛。仿真研究证明了所提出方法的优越性能。最后，我们将我们的方法应用于威斯康星大学人口健康信息交换数据库。