WaveGNN: 将图神经网络与变换器结合用于不规则临床时间序列的衰减感知分类
WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series
AI总结：
WaveGNN通过结合图神经网络和衰减感知变换器，直接处理不规则临床时间序列，生成稀疏可解释图以提升分类性能和临床决策支持。
摘要：
临床时间序列通常以不规则的方式采样，具有变化的传感器频率、缺失观测和时间戳不一致。先前的方法通常通过将数据插值为规则序列来解决这些不规则性，从而引入偏差，或者通过生成不一致且不可解释的关系跨传感器测量，使准确学习系列内和系列间依赖关系变得更加复杂。我们介绍了WaveGNN，一个直接在不规则多变量时间序列上运行的模型，无需插值或转换为规则表示。WaveGNN结合了衰减感知的Transformer以捕捉系列内动态，以及一个特定于样本的图神经网络，该网络建模了短期和长期的传感器间关系。因此，它为每个样本生成一个单一、稀疏且可解释的图。在多个基准数据集（P12、P19、MIMIC-III和PAM）上，WaveGNN持续表现出强劲的性能，而其他最先进的基线方法在某些数据集或任务上表现良好，但在其他情况下表现不佳。虽然WaveGNN不一定在每种情况下都超越所有方法，但其在多样化设置中的一致性和鲁棒性使其脱颖而出。此外，学习到的图与已知的生理结构很好地对齐，增强了可解释性并支持临床决策。
英文摘要：
Clinical time series are often irregularly sampled, with varying sensor frequencies, missing observations, and misaligned timestamps. Prior approaches typically address these irregularities by interpolating data into regular sequences, thereby introducing bias, or by generating inconsistent and uninterpretable relationships across sensor measurements, complicating the accurate learning of both intra-series and inter-series dependencies. We introduce WaveGNN, a model that operates directly on irregular multivariate time series without interpolation or conversion to a regular representation. WaveGNN combines a decay-aware Transformer to capture intra-series dynamics with a sample-specific graph neural network that models both short-term and long-term inter-sensor relationships. Therefore, it generates a single, sparse, and interpretable graph per sample. Across multiple benchmark datasets (P12, P19, MIMIC-III, and PAM), WaveGNN delivers consistently strong performance, whereas other state-of-the-art baselines tend to perform well on some datasets or tasks but poorly on others. While WaveGNN does not necessarily surpass every method in every case, its consistency and robustness across diverse settings set it apart. Moreover, the learned graphs align well with known physiological structures, enhancing interpretability and supporting clinical decision-making.
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。
