TT-XAI：通过关键词蒸馏与LLM推理实现可信的临床文本解释
TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning
AI总结：
本文提出TT-XAI框架，通过领域感知的关键词蒸馏和LLM推理，显著提升了临床语言模型在电子健康记录上的分类性能与解释保真度，为临床决策支持提供了可信且可扩展的AI路径。
摘要：
临床语言模型在处理冗长、非结构化的电子健康记录（EHRs）时，往往难以提供可信的预测和解释。本研究提出了TT-XAI，这是一个轻量且有效的框架，通过领域感知的关键词蒸馏和大型语言模型（LLMs）推理，同时提升了分类性能和可解释性。首先，我们证明了将原始出院记录蒸馏为简洁的关键词表示，能够显著提升BERT分类器的性能，并通过LIME的聚焦变体提高局部解释的保真度。其次，我们使用关键词引导的提示来控制LLMs，生成了思维链临床解释，从而产生更简洁且与临床相关的推理。我们使用基于删除的保真度指标、通过LLaMA-3评分的自我评估，以及领域专家参与的盲测人类研究来评估解释质量。所有评估方式均一致倾向于关键词增强方法，证实了蒸馏提升了机器和人类的可解释性。TT-XAI为临床决策支持中可信、可审计的AI提供了可扩展的路径。
英文摘要：
Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introduces TT-XAI, a lightweight and effective framework that improves both classification performance and interpretability through domain-aware keyword distillation and reasoning with large language models (LLMs). First, we demonstrate that distilling raw discharge notes into concise keyword representations significantly enhances BERT classifier performance and improves local explanation fidelity via a focused variant of LIME. Second, we generate chain-of-thought clinical explanations using keyword-guided prompts to steer LLMs, producing more concise and clinically relevant reasoning. We evaluate explanation quality using deletion-based fidelity metrics, self-assessment via LLaMA-3 scoring, and a blinded human study with domain experts. All evaluation modalities consistently favor the keyword-augmented method, confirming that distillation enhances both machine and human interpretability. TT-XAI offers a scalable pathway toward trustworthy, auditable AI in clinical decision support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
