Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching

This paper introduces a lightweight framework for scalable patient-trial matching, addressing challenges posed by long, complex electronic health records. It combines retrieval-augmented generation (RAG) to identify relevant EHR segments with large language models (LLMs) to encode these segments into informative representations, improving efficiency and generalization.