DBGL models irregular medical time series via patient-variable bipartite graphs and node-specific temporal decay encoding to avoid artificial alignment and capture decay rates, outperforming baselines on four public datasets.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification
DBGL models irregular medical time series via patient-variable bipartite graphs and node-specific temporal decay encoding to avoid artificial alignment and capture decay rates, outperforming baselines on four public datasets.