We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks. The code will be published at \url{https://github.com/ritaranx/RAM-EHR}.

翻译：本文提出RAM-EHR（检索增强流程），旨在提升电子健康记录（EHR）的临床预测性能。RAM-EHR首先整合多源知识库并将其转换为文本格式，进而通过稠密检索获取与医学概念相关的信息。该策略有效解决了复杂医学概念命名带来的识别难题。随后，RAM-EHR通过一致性正则化协同训练增强本地EHR预测模型，以融合患者就诊记录与知识库摘要中的互补信息。在两个EHR数据集上的实验表明，RAM-EHR相较于现有知识增强基线方法具有显著优势（AUROC提升3.4%，AUPR提升7.2%），凸显了其知识摘要机制在临床预测任务中的有效性。相关代码将发布于 \url{https://github.com/ritaranx/RAM-EHR}。