Retrieval-Augmented Generation (RAG) improves large language models by retrieving external knowledge, often truncated into smaller chunks due to the input context window, which leads to information loss, resulting in response hallucinations and broken reasoning chains. Moreover, traditional RAG retrieves unstructured knowledge, introducing irrelevant details that hinder accurate reasoning. To address these issues, we propose TAdaRAG, a novel RAG framework for on-the-fly task-adaptive knowledge graph construction from external sources. Specifically, we design an intent-driven routing mechanism to a domain-specific extraction template, followed by supervised fine-tuning and a reinforcement learning-based implicit extraction mechanism, ensuring concise, coherent, and non-redundant knowledge integration. Evaluations on six public benchmarks and a real-world business benchmark (NowNewsQA) across three backbone models demonstrate that TAdaRAG outperforms existing methods across diverse domains and long-text tasks, highlighting its strong generalization and practical effectiveness.

Recent advances in clinical prediction leverage large language models (LLMs) to extract semantic information from Electronic Health Records (EHRs). However, LLMs could produce biased or hallucinated responses camouflaged by their fluency and realistic appearance, which is unacceptable in diagnosis prediction. Uncertainty estimation (UE) has emerged as an effective approach to address this challenge by quantifying hallucination levels and prediction confidence in LLM outputs. Yet, directly determining diagnosis predictions based on UE remains insufficient, as diagnoses with high uncertainty may still correspond to correct outcomes. To this end, we propose ULoR, an uncertainty-aware leave-one-out refinement framework for reliable diagnosis prediction. Specifically, we first compute the UE scores by integrating statistical information from multiple samples of the model's diagnosis ranking distributions and leverage these scores for initial predictions. Then, guided by the leave-one-out strategy, we construct multiple-choice tasks for high-uncertainty diagnoses using external syndrome knowledge and fine-tune the refinement component to resolve them, thereby confirming or replacing uncertain predictions. Extensive experiments on two real-world EHR datasets demonstrate that ULoR consistently outperforms state-of-the-art baselines, showcasing its practical utility in real-world clinical settings.

Accurate diagnosis prediction using Electronic Health Records (EHRs) is essential for personalized healthcare. Clinical knowledge graphs (KGs) can enrich EHRs by structuring medical knowledge, and recent work integrates large language models (LLMs) with KGs to enhance reasoning. However, these approaches often depends on static, expensive global graph construction and one-time retrieval, yielding noisy or irrelevant subgraphs that hinder effective diagnosis prediction in real-world clinical scenarios. To this end, we propose$\mathrm{D}^{2}$KGMed, a diagnosis prediction framework that constructs a patient-specific Dynamic Diagnostic Knowledge Graph guided by LLMs. It consists of two stages: constructing an initial graph from diagnostic entities and multi-source medical knowledge; refining its construction via supervised fine-tuning to better align with the ideal graph for conciseness and relevance, and subsequently leveraging it for interpretable predictions. This design reduces graph construction costs and retrieval noise common in KG+LLM methods, enabling more accurate diagnosis prediction. Extensive experiments on two real-world EHR datasets demonstrate that D2KGMed outperforms state-of-the-art baselines, especially in few-shot learning scenarios, showcasing its practical utility in real-world clinical settings.