  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.

Machine Learning;Computers and Society