  In this work, we introduce the Multiple Embedding Model for EHR (MEME), an
approach that serializes multimodal EHR tabular data into text using
pseudo-notes, mimicking clinical text generation. This conversion not only
preserves better representations of categorical data and learns contexts but
also enables the effective employment of pretrained foundation models for rich
feature representation. To address potential issues with context length, our
framework encodes embeddings for each EHR modality separately. We demonstrate
the effectiveness of MEME by applying it to several decision support tasks
within the Emergency Department across multiple hospital systems. Our findings
indicate that MEME outperforms traditional machine learning, EHR-specific
foundation models, and general LLMs, highlighting its potential as a general
and extendible EHR representation strategy.
