发表机构
Old Dominion University; University of Arkansas for Medical Sciences（欧道明大学; 阿肯色大学医学科学分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出知识增强的结构化EHR特征，融合四个医学知识源，在不使用临床记录的情况下，以较低计算成本实现与现有方法相当的30天再入院预测性能（AUROC 0.743）。
AI 中文摘要
近期针对30天再入院预测的方法依赖于对出院小结应用预训练语言模型。尽管这些方法取得了较强的性能，但它们依赖于临床记录的可用性，产生大量的计算成本，并且生成的表示缺乏可解释性。我们提出了一种知识增强的特征表示，该表示通过四个医学知识源增强结构化电子健康记录（EHR）数据：疾病本体映射、手术分类、药物成分词汇和器官系统实验室聚合，而不使用临床记录。每个特征维度对应一个命名的临床概念，从而产生稀疏且可解释的患者表示。该方法在MIMIC-IV v2.2队列上使用六个分类器进行评估。在20折交叉验证下，最佳配置实现了0.743的AUROC。这一性能与此前在该数据集上报告的方法（包括仅使用结构化数据的方法以及结合临床记录的方法）相当，同时所需计算成本显著更低。可解释性分析表明，人口统计学特征、器官系统实验室、药物成分特征和一级本体疾病类别驱动预测，而更深层次的层级贡献可忽略不计。这些发现表明，知识增强的结构化特征为30天再入院预测提供了一种具有竞争力和高效性的临床记录嵌入替代方案。
英文摘要
Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods achieve strong performance, they depend on the availability of clinical notes, incur substantial computational costs, and yield representations that lack interpretability. We propose a knowledge-enriched feature representation that augments structured Electronic Health Record (EHR) data with four medical knowledge sources: disease ontology mapping, procedure classification, drug ingredient vocabulary, and organ system laboratory aggregation, without using clinical notes. Each feature dimension corresponds to a named clinical concept, yielding a sparse and interpretable patient representation. The approach is evaluated with six classifiers on a MIMIC-IV v2.2 cohort. Under 20-fold cross-validation, the best configuration achieves an AUROC of 0.743. This performance is comparable to that of previously reported methods on this dataset, including both those using only structured data and those incorporating clinical notes, while requiring considerably less computational cost. Interpretability analysis shows that demographics, organ system labs, drug ingredient features, and first-level ontology disease categories drive prediction, while deeper hierarchy levels contribute negligibly. These findings indicate that knowledge-enriched structured features offer a competitive and efficient alternative to embeddings from clinical notes for 30-day readmission prediction.
Comments15 pages, 3 figures. Accepted at SDSC 2026 Mid-Atlantic
