In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. This approach incorporates "pseudo-notes", textual representations of tabular EHR concepts such as diagnoses and medications, and allows us to effectively employ Large Language Models (LLMs) for EHR representation. This framework also adopts a multimodal approach, embedding each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality embedding methods and traditional machine learning approaches. However, we also observe notable limitations in generalizability across hospital institutions for all tested models.

翻译：本研究提出了面向电子健康记录的多嵌入模型（MEME），该方法将电子健康记录（EHR）视为多模态数据。该模型引入"伪笔记"概念，将诊断、用药等表格型EHR概念转化为文本表征，从而有效利用大语言模型（LLMs）进行EHR表征。该框架采用多模态方法，分别对每种EHR模态进行独立嵌入。我们在多个医院系统的急诊科多项任务中验证了MEME的有效性。研究结果表明，MEME在性能上超越了单一模态嵌入方法和传统机器学习方法。然而，我们也观察到所有测试模型在医院机构间的泛化能力存在显著局限性。