CoFE：一种生成反事实心电图以用于可解释心脏AI诊断的框架
CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
AI总结：
本文提出CoFE框架，通过生成反事实心电图揭示振幅、间期等特征对AI-ECG模型预测决策的影响，并在房颤分类和钾水平回归两个案例中验证其可解释性与临床一致性。
摘要：
认识到需要可解释人工智能（XAI）方法来实现基于AI的心电图预测模型（AI-ECG）成功融入临床实践，我们引入了一个生成反事实心电图（即命名为CoFE）的框架，以说明特定特征（如振幅和间期）如何影响模型的预测决策。为了证明CoFE的适用性，我们展示了两个案例研究：心房颤动分类和钾水平回归模型。CoFE揭示了与已确立的临床知识一致的心电图信号特征变化。通过阐明有效特征在心电图中出现的位置以及它们如何影响模型的预测，我们预期该框架将增强AI-ECG模型的可解释性，并支持更有效的临床决策。我们的演示视频可在以下网址获取：https://www.youtube.com/watch?v=YoW0bNBPglQ。
英文摘要：
Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models (AI-ECG) into clinical practice, we introduce a framework generating \textbf{Co}unter\textbf{F}actual \textbf{E}CGs (i,e., named CoFE) to illustrate how specific features, such as amplitudes and intervals, influence the model's predictive decisions. To demonstrate the applicability of the CoFE, we present two case studies: atrial fibrillation classification and potassium level regression models. The CoFE reveals feature changes in ECG signals that align with the established clinical knowledge. By clarifying both \textbf{where valid features appear} in the ECG and \textbf{how they influence the model's predictions}, we anticipate that our framework will enhance the interpretability of AI-ECG models and support more effective clinical decision-making. Our demonstration video is available at: https://www.youtube.com/watch?v=YoW0bNBPglQ.
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。
