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.

我们介绍了一种多嵌入模型 (Multiple Embedding Model) 用于电子健康记录 (EHR)，该模型将 EHR 视为多模态数据，并使用大型语言模型 (LLM) 来表示 EHR。通过将每个 EHR 模态嵌入到一个统一的框架中，我们展示了 MEME 的有效性，并在多个医院系统的急诊科中应用。结果显示 MEME 的性能优于单模态嵌入方法和传统机器学习方法，但我们也观察到在不同医院机构间的泛化能力方面存在明显限制。