用沙普利值解释患者风险变化原因，实时精准定位关键因素。
DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values
- 基于沙普利值改进时序解释方法，捕捉特征协同效应。
- 在MIMIC-III数据集上提升解释质量62%，计算速度加快33%。
- 适合临床医生快速理解患者风险动态，提升干预效率。
本研究提出DeltaSHAP，一种专为在线患者监测系统设计的可解释人工智能算法。在临床环境中，揭示驱动患者风险演变的原因对及时干预至关重要，但现有XAI方法无法满足临床时间序列解释的独特需求。为此，DeltaSHAP解决三大临床需求：解释连续预测的变化而非孤立预测分数，提供特征归因的大小与方向，实现实时反馈。通过将沙普利值适配至时序场景，该方法准确捕捉特征组合效应，并仅使用实际观测到的特征组合进行归因，兼顾效率与实用性。我们还引入新的评估指标，用于衡量在线时间序列解释的忠实性。实验表明，在MIMIC-III脱机风险预测任务中，DeltaSHAP在解释质量上优于当前最佳方法62%，计算效率提升33%。代码已开源：https://github.com/AITRICS/DeltaSHAP。
原文摘要 · Abstract (English)
This study proposes DeltaSHAP, a novel explainable artificial intelligence (XAI) algorithm specifically designed for online patient monitoring systems. In clinical environments, discovering the causes driving patient risk evolution is critical for timely intervention, yet existing XAI methods fail to address the unique requirements of clinical time series explanation tasks. To this end, DeltaSHAP addresses three key clinical needs: explaining the changes in the consecutive predictions rather than isolated prediction scores, providing both magnitude and direction of feature attributions, and delivering these insights in real time. By adapting Shapley values to temporal settings, our approach accurately captures feature coalition effects. It further attributes prediction changes using only the actually observed feature combinations, making it efficient and practical for time-sensitive clinical applications. We also introduce new evaluation metrics to evaluate the faithfulness of the attributions for online time series, and demonstrate through experiments on online patient monitoring tasks that DeltaSHAP outperforms state-of-the-art XAI methods in both explanation quality as 62% and computational efficiency as 33% time reduction on the MIMIC-III decompensation benchmark. We release our code at https://github.com/AITRICS/DeltaSHAP.
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