Lightweight RAG plus LLM framework for patient-trial matching reduces computational cost while matching the performance of end-to-end LLM approaches on clinical benchmarks and real-world data.
Figure 2 summarizes performance across structured-only, unstructured-only, and mixed EHR settings using Macro-F1, AUROC, and AUPRC
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Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching
Lightweight RAG plus LLM framework for patient-trial matching reduces computational cost while matching the performance of end-to-end LLM approaches on clinical benchmarks and real-world data.