English(EN) A Concept-Based Interpretable Framework for Clinical Text-based Prediction via Token-Concept Attention without Concept Supervision

新框架可在无概念监督的情况下实现可解释的临床人工智能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种新的框架，用于解释语言模型在临床文本预测中的结果，而无需概念监督。该方法使用交叉注意力瓶颈来估计每个文本令牌的概念相关性，从而实现可解释的预测，同时保持强大的性能。该框架已在 MIMIC-III 数据集上针对胸部创伤严重程度分层和院内死亡率预测等任务进行了测试，其性能接近黑盒模型，并提供了队列和患者级别的可解释性。 AI

影响提高了临床人工智能预测的透明度，可能带来更值得信赖的诊断工具。

排序理由关于人工智能可解释性新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jifan Gao, Junjie Hu, John Caskey, Madeline Oguss, Simona Cristea, Anoop Mayampurath, Matthew Churpek, Majid Afshar, Guanhua Chen · 2026-11-11 05:00

一种基于概念的可解释框架，用于通过无概念监督的令牌-概念注意力进行临床文本预测

arXiv:2611.13564v1 Announce Type: cross Abstract: Objective: Concept-based interpretation has emerged as a promising approach for explaining blackbox models using human-understandable, high-level concepts. However, most existing methods require annotated concept labels to train t…

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报道来源 [1]

一种基于概念的可解释框架，用于通过无概念监督的令牌-概念注意力进行临床文本预测

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