Accurately predicting hospital readmission risks using electronic health records (EHRs) is critical for effective patient management and healthcare resource allocation. Patient populations in health systems are highly heterogeneous across different primary diagnoses, necessitating tailored yet interpretable prediction models. We propose a hierarchical modeling framework incorporating hierarchical nested re-parameterization and structured regularization methods, which we call hierNest. Specifically, our approach leverages the inherent hierarchical structure present in primary diagnoses and groupings of these diagnoses into major diagnostic categories. Our methodology facilitates information borrowing across related patient subgroups and preserves interpretability at different hierarchical levels. Simulation studies demonstrate superior predictive accuracy of the proposed method, particularly with small subgroup sample sizes and varying degrees of hierarchical effects. We apply our methods to a large EHR dataset comprising Medicare patients.

翻译：准确利用电子健康档案（EHRs）预测医院再入院风险，对于优化患者管理和医疗资源配置至关重要。不同主要诊断类别的患者群体具有高度异质性，因此需要构建既具针对性又可解释的预测模型。我们提出一个包含层次化嵌套重参数化与结构化正则化方法的分层建模框架，称为hierNest。具体而言，该方法充分利用主要诊断及其归入主要诊断类别时所固有的层次结构，促进相关患者子组间的信息共享，并在不同分层级别保持可解释性。模拟研究表明，尤其在子组样本量较小且层次效应变化显著的场景下，所提方法具有更优的预测精度。我们将该方法应用于包含医疗保险患者的大型EHR数据集。