Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)

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 …

clinical concepts cs.cl data ehr electronic electronic health records embedding emergency health language large language meme multimodal multimodal data multiple notes prediction records tabular tasks textual work