用可验证逻辑解释模型，提升查加斯心肌病猝死预测可信度
Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
- 采用有正确性保障的逻辑方法解释高精度AI模型决策过程
- 在95%以上准确率下实现100%解释保真度
- 适合临床医生和资源匮乏地区医疗系统使用
猝死（SCD）难以预测，尤其在未被归类为高风险的查加斯心肌病（CC）患者中更具挑战性。尽管人工智能与机器学习模型提升了风险分层能力，但其应用受限于缺乏透明性，常被视为决策过程不透明的“黑箱”。部分方法虽提供启发式解释，却无正确性保证，易导致误判。为此，本文将具备正确性保障的逻辑可解释性方法应用于CC患者的SCD预测任务。该方法作用于一个准确率和召回率均超95%的AI分类器，表现出优异的预测性能与100%的解释保真度。相较于现有最优启发式方法，其解释具更强一致性与鲁棒性。该方法增强了临床信任，推动了AI工具在实际诊疗中的集成与大规模部署，尤其适用于该病高发的流行区域。
原文摘要 · Abstract (English)
Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. While AI and machine learning models improve risk stratification, their adoption is hindered by a lack of transparency, as they are often perceived as \textit{black boxes} with unclear decision-making processes. Some approaches apply heuristic explanations without correctness guarantees, leading to mistakes in the decision-making process. To address this, we apply a logic-based explainability method with correctness guarantees to the problem of SCD prediction in CC. This explainability method, applied to an AI classifier with over 95\% accuracy and recall, demonstrated strong predictive performance and 100\% explanation fidelity. When compared to state-of-the-art heuristic methods, it showed superior consistency and robustness. This approach enhances clinical trust, facilitates the integration of AI-driven tools into practice, and promotes large-scale deployment, particularly in endemic regions where it is most needed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。