Lightweight LLM Framework for Scalable Patient-Trial Matching
A new lightweight framework combining retrieval-augmented generation (RAG) and large language models (LLMs) aims to improve patient-trial matching by efficiently processing long electronic health records (EHRs) and complex eligibility criteria. The approach separates RAG to identify clinically relevant EHR segments, reducing input complexity, while LLMs encode these segments into informative representations. This addresses scalability and computational efficiency challenges faced by full-document LLM processing and traditional machine learning methods. The framework is detailed in arXiv preprint 2604.22061.
Key facts
- arXiv:2604.22061 proposes a lightweight RAG and LLM-based framework for patient-trial matching.
- The framework separates RAG for segment identification and LLMs for encoding.
- It aims to overcome scalability and computational inefficiency of full-document LLM processing.
- Traditional machine learning methods struggle with unstructured clinical narratives.
- The approach reduces input complexity by focusing on clinically relevant EHR segments.
- Patient-trial matching requires reasoning over long, heterogeneous EHRs and complex eligibility criteria.
- The framework is designed for scalable and generalizable patient-trial matching.
Entities
Institutions
- arXiv