Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

This research introduces "Schema-Adaptive Tabular Representation Learning," a novel method using Large Language Models (LLMs) to generate transferable tabular embeddings. By semantically encoding structured variables into natural language, it enables zero-shot alignment across varying EHR schemas in clinical medicine without manual feature engineering.