面向生物医学研究与临床支持的基于深度思考LLM的检索增强知识挖掘方法
A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support
AI总结：
提出基于深度思考LLM的检索增强知识挖掘方法，通过构建BioStrataKG和BioCDQA，并引入IP-RAR框架提升检索与推理能力，显著优于现有方法，助力临床与科研决策。
摘要：
知识图谱和大语言模型（LLM）是生物医学知识集成与推理的关键工具，有助于科学文章的结构化组织和复杂语义关系的发现。然而，当前方法面临挑战：知识图谱构建受限于复杂术语、数据异质性和知识快速演化，而LLM在检索和推理方面存在局限，难以揭示跨文档关联和推理路径。为解决这些问题，我们提出一种流程，使用LLM从大规模文章构建生物医学知识图谱（BioStrataKG），并构建跨文档问答数据集（BioCDQA）以评估潜在知识检索和多跳推理。随后，我们引入集成与渐进式检索增强推理（IP-RAR）来提升检索准确性和知识推理能力。IP-RAR通过基于集成推理的检索最大化信息召回，并利用基于渐进推理的生成优化知识，采用自我反思实现深度思考和精确的上下文理解。实验表明，与现有方法相比，IP-RAR将文档检索F1分数提升20%，答案生成准确率提升25%。该框架帮助医生高效整合治疗证据以制定个性化用药方案，并使研究人员能够分析进展与研究空白，加速科学发现与决策。
英文摘要：
Knowledge graphs and large language models (LLMs) are key tools for biomedical knowledge integration and reasoning, facilitating structured organization of scientific articles and discovery of complex semantic relationships. However, current methods face challenges: knowledge graph construction is limited by complex terminology, data heterogeneity, and rapid knowledge evolution, while LLMs show limitations in retrieval and reasoning, making it difficult to uncover cross-document associations and reasoning pathways. To address these issues, we propose a pipeline that uses LLMs to construct a biomedical knowledge graph (BioStrataKG) from large-scale articles and builds a cross-document question-answering dataset (BioCDQA) to evaluate latent knowledge retrieval and multi-hop reasoning. We then introduce Integrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) to enhance retrieval accuracy and knowledge reasoning. IP-RAR maximizes information recall through Integrated Reasoning-based Retrieval and refines knowledge via Progressive Reasoning-based Generation, using self-reflection to achieve deep thinking and precise contextual understanding. Experiments show that IP-RAR improves document retrieval F1 score by 20\% and answer generation accuracy by 25\% over existing methods. This framework helps doctors efficiently integrate treatment evidence for personalized medication plans and enables researchers to analyze advancements and research gaps, accelerating scientific discovery and decision-making.
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