Irregular multivariate time series data is prevalent in the clinical and healthcare domains. It is characterized by time-wise and feature-wise irregularities, making it challenging for machine learning methods to work with. To solve this, we introduce a new model architecture composed of two modules: (1) DLA, a Dynamic Local Attention mechanism that uses learnable queries and feature-specific local windows when computing the self-attention operation. This results in aggregating irregular time steps raw input within each window to a harmonized regular latent space representation while taking into account the different features' sampling rates. (2) A hierarchical MLP mixer that processes the output of DLA through multi-scale patching to leverage information at various scales for the downstream tasks. Our approach outperforms state-of-the-art methods on three real-world datasets, including the latest clinical MIMIC IV dataset.

翻译：非规则多变量时间序列数据在临床和医疗领域普遍存在。其时间维度和特征维度的非规则性给机器学习方法带来严峻挑战。为解决此问题，我们提出一种由两个模块构成的新型模型架构：(1) DLA动态局部注意力机制——通过可学习查询向量和特征特异性局部窗口执行自注意力运算，在考虑不同特征采样率的前提下，将每个窗口内非规则时间步的原始输入聚合为规整的潜在空间表示；(2) 分层MLP混合器——通过多尺度分块处理DLA的输出，为下游任务提取不同尺度的信息特征。该方法在三个真实世界数据集（含最新临床MIMIC IV数据集）上均优于现有最优方法。