用关键词提炼+大模型推理，让医疗文本解释更可信、更易懂。
TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning
- 从病历中提取关键信息，压缩成关键词提升模型性能。
- 关键词引导大模型生成简洁且符合临床逻辑的推理链。
- 在机器与专家评价中均优于传统方法，适合临床决策支持场景。
临床语言模型在处理长篇无结构电子健康记录（EHR）时，常难以提供可信的预测与解释。本文提出轻量高效的TT-XAI框架，通过领域感知的关键词提炼与大语言模型（LLM）推理，同时提升分类性能与可解释性。首先，将原始出院小结提炼为简洁关键词表示，显著提升BERT分类器表现，并通过改进版LIME增强局部解释的保真度。其次，利用关键词引导提示，驱动LLM生成链式思维临床解释，使推理更紧凑且贴近临床实际。通过删除法保真度指标、LLaMA-3自评及盲测专家评估，多维度验证表明关键词增强方法在解释质量上全面占优，证实其对机器与人类可解释性的双重提升。TT-XAI为临床决策支持中的可信、可审计AI提供了可扩展路径。
原文摘要 · Abstract (English)
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.
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