A retrieval-augmented knowledge mining method with deep thinking LLMs for biomedical research and clinical support.

Background: 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.

Results: We propose a pipeline that uses LLMs to construct a Biomedical Stratified Knowledge Graph (BioStrataKG) from large-scale articles and builds the Biomedical Cross-Document Question Answering Dataset (BioCDQA) to evaluate latent knowledge retrieval and multihop 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.

Conclusions: The IP-RAR helps doctors efficiently integrate treatment evidence to inform the development of personalized medication plans and enables researchers to analyze advancements and research gaps, accelerating the hypothesis generation phase of scientific discovery and decision-making.