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.
Our primary evaluation focuses on precision, recall, and Macro-F1 scores for the binary classification task of determining whether a patient meets each of the eligibility criteria
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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.