GraphWalker framework boosts LLM clinical reasoning with patient analogy

Researchers have developed GraphWalker, a new framework designed to enhance clinical reasoning in large language models (LLMs) when analyzing electronic health records (EHRs). This method addresses limitations in existing patient analogy approaches by integrating both data-driven and model-driven perspectives, discovering patient cohorts for structured retrieval, and optimizing demonstration selection for maximum information gain. Experiments show GraphWalker outperforms current baselines and maintains robustness across different datasets, functioning as a pluggable skill for LLM-based clinical workflows. AI

IMPACT Enhances LLM capabilities in healthcare by improving clinical reasoning and patient analogy for EHR analysis.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM clinical reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Electronic Health Records
• GraphWalker
• Large Language Models
• Yue Fang

• paper
• product

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