Healthcare prediction using electronic health records (EHRs) is a cornerstone of modern clinical decision support. Conventional machine learning (ML) models achieve strong predictive performance but rarely provide knowledge-grounded, human-readable explanations. Large language models (LLMs) incorporated with medical knowledge graphs (KGs) offer advanced reasoning and explainability with encoded clinical facts, yet existing approaches typically perform monolithic reasoning over entire patient records, mixing task-irrelevant context and obscuring disease-specific attribution. To bridge these gaps, we propose CRU-Reason, a novel framework that integrates ML models, medical KGs, and LLMs through Clinical Rationale Units (CRUs) for knowledge-grounded and interpretable healthcare prediction. CRU is a localized structure that explicitly captures clinically meaningful communities among EHR concepts, thereby enabling LLMs to perform disease-centered, fine-grained reasoning. Experiments on two public clinical datasets demonstrate that CRU-Reason improves over the strongest evaluated EHR-prediction baselines by an average of +9.4% across drug recommendation and diagnosis prediction, while exhibiting improved rationale quality and cross-dataset robustness.

Large reasoning models (LRMs) achieve strong performance on complex tasks by generating intermediate reasoning before the final answer, yet they remain prone to reasoning hallucinations such as subtle arithmetic or constraint-violation errors. Prior hallucination detectors often rely on external verification or local token-level signals, which are limited for LRMs and largely overlook whether the cross-phase information flow from reasoning to answering is structurally robust. We propose Routing Focus Score (RFS), a step-level indicator that measures how strongly cross-step attention routing aligns with semantic proximity derived from hidden-state cosine similarity. We further design RFS-Guard, a lightweight hallucination detection framework based on RFS. Empirically, we observe that higher reasoning–answer RFS is consistently associated with higher hallucination risk, suggesting a routing-collapse failure mode where models might prefer self-confirmation loops and suppress the ability to audit their own generations. Experimental results across multiple domains and models demonstrate the superiority of RFS-Guard for detecting and localizing hallucinations in LRMs without requiring external tools or repeated sampling.

While Large Language Models (LLMs) have exhibited impressive performance in generating long-form content, they frequently present a hazard of producing factual inaccuracies or hallucinations. An effective strategy to mitigate this hazard is to leverage off-the-shelf LLMs to detect hallucinations after the generation. The primary challenge resides in the comprehensive elicitation of the intrinsic knowledge acquired during their pre-training phase. However, existing methods that employ multi-step reasoning chains predominantly fall short of addressing this issue. Moreover, since existing methods for hallucination detection tend to decompose text into isolated statements, they are unable to understand the contextual semantic relations in long-form content. In this paper, we study a novel concept, self-elicitation, to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics. We present a framework, SelfElicit, to integrate self-elicitation with graph structures to effectively organize the elicited knowledge and facilitate factual evaluations. Extensive experiments on five datasets in various domains demonstrate the effectiveness of self-elicitation and the superiority of our proposed method.