irregular data

DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification

DBGL introduces a novel Decay-Aware Bipartite Graph Learning method to address the challenges of irregular medical time series classification. It utilizes a patient-variable bipartite graph to model irregular sampling patterns and variable relationships, alongside a node-specific temporal decay encoding for variable decay irregularity.