面向不规则多变量时间序列的可解释时空图卷积神经网络：架构及其在ICU患者数据中的应用
Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data
AI总结：
该研究提出可解释时空图卷积神经网络XST-GCNN，用于处理异构不规则多变量时间序列，在ICU患者多重耐药性预测任务中性能优于传统模型，同时具备临床可解释性。
摘要：
本文提出了XST-GCNN（可解释时空图卷积神经网络），这是一种用于处理异构不规则多变量时间序列（MTS）数据的新型架构。我们的方法通过利用一种采用时空图的图卷积神经网络（GCNN），在统一的时空流水线中捕获时间依赖关系与特征依赖关系，旨在优化预测准确性与互操作性。在图估计方面，我们引入了多种技术，其中包括一种基于（异构）Gower距离的方法。图估计完成后，我们提出了两种图构建方法：一种基于笛卡尔积，对各时刻做同质化处理；另一种是时空方法，每个时间步都对应不同的图。我们还提出了两种GCNN架构：一种是带归一化邻接矩阵的标准GCNN，另一种是高阶多项式GCNN。除了准确性之外，我们还通过设计固有可解释的模型并开展全面的可解释性分析来强调可解释性，识别出驱动预测的关键特征-时间组合。我们使用富恩拉夫拉达大学医院的真实电子健康记录数据评估XST-GCNN，用于预测ICU患者的多重耐药性（MDR）——这是一项与高死亡率和复杂治疗相关的关键医疗挑战。我们的架构性能优于传统模型，取得了81.03±2.43的平均ROC-AUC分数。此外，可解释性分析为驱动MDR预测的临床因素提供了可付诸实践的见解，提升了模型透明度。这项工作为利用异构MTS解决复杂推理任务树立了基准，为现实世界应用提供了一种通用、可解释的解决方案。
英文摘要：
In this paper, we present XST-GCNN (eXplainable Spatio-Temporal Graph Convolutional Neural Network), a novel architecture for processing heterogeneous and irregular Multivariate Time Series (MTS) data. Our approach captures temporal and feature dependencies within a unified spatio-temporal pipeline by leveraging a GCNN that uses a spatio-temporal graph aimed at optimizing predictive accuracy and interoperability. For graph estimation, we introduce techniques, including one based on the (heterogeneous) Gower distance. Once estimated, we propose two methods for graph construction: one based on the Cartesian product, treating temporal instants homogeneously, and another spatio-temporal approach with distinct graphs per time step. We also propose two GCNN architectures: a standard GCNN with a normalized adjacency matrix and a higher-order polynomial GCNN. In addition to accuracy, we emphasize explainability by designing an inherently interpretable model and performing a thorough interpretability analysis, identifying key feature-time combinations that drive predictions. We evaluate XST-GCNN using real-world Electronic Health Record data from University Hospital of Fuenlabrada to predict Multidrug Resistance (MDR) in ICU patients, a critical healthcare challenge linked to high mortality and complex treatments. Our architecture outperforms traditional models, achieving a mean ROC-AUC score of 81.03 +- 2.43. Furthermore, the interpretability analysis provides actionable insights into clinical factors driving MDR predictions, enhancing model transparency. This work sets a benchmark for tackling complex inference tasks with heterogeneous MTS, offering a versatile, interpretable solution for real-world applications.
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。
