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English(EN) From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection

新框架使用于认知障碍的AI语音分析在临床上更具可解释性

研究人员开发了一个多阶段可解释性框架,以使用于语音认知障碍检测的基于Transformer的模型在临床使用中更具可解释性。该框架集成了基于SHAP的token归因和语言特征,并使用LLaMA-3.1-70B-Instruct构建了一个LLM推理管道。该系统基于SpeechCARE-Adaptive Gating Network构建,在NIA PREPARE基准测试上达到了72.11%的F1分数,并通过82/100的系统可用性量表分数证明了其在临床工作流程集成方面的高潜力。 AI

影响 增强了医疗保健领域AI模型的可解释性,可能导致AI在认知障碍检测中的临床应用更广泛。

排序理由 该集群包含一篇详细介绍新框架和方法的学术论文。

在 arXiv cs.AI 阅读 →

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新框架使用于认知障碍的AI语音分析在临床上更具可解释性

报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh ·

    Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection

    arXiv:2606.30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging. We propose a multimodal framework leveraging Whisper for d…

  2. arXiv cs.AI TIER_1 English(EN) · Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh ·

    用于通过多视图语音衍生特征检测痴呆症的 LoRA 微调大语言模型

    arXiv:2606.28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension …

  3. arXiv cs.AI TIER_1 English(EN) · Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori ·

    从黑箱到临床洞察:用于语音认知障碍检测的多阶段可解释框架

    arXiv:2606.27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework…

  4. arXiv cs.AI TIER_1 English(EN) · Maryam Zolnoori ·

    从黑箱到临床洞察:用于语音认知障碍检测的多阶段可解释框架

    Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework that translates black-box transformer predictions…