MedicalBench is a benchmark for implicit medical concept extraction and sentence-level evidence retrieval built from MIMIC-IV discharge summaries with human verification to test LLM reasoning on unstated medical ideas.
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2 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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RDMA equips small LLMs with abbreviation resolution, phenotype reasoning, and ontology tools to mine rare diseases from EHR notes, outperforming fine-tuned and RAG baselines at up to 10x lower inference cost.
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MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction
MedicalBench is a benchmark for implicit medical concept extraction and sentence-level evidence retrieval built from MIMIC-IV discharge summaries with human verification to test LLM reasoning on unstated medical ideas.
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RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
RDMA equips small LLMs with abbreviation resolution, phenotype reasoning, and ontology tools to mine rare diseases from EHR notes, outperforming fine-tuned and RAG baselines at up to 10x lower inference cost.