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This paper introduces DNBNet, a debiased neural basis-function network for irregular time series forecasting, addressing limitations in existing methods by correcting asymptotic bias and using adaptive neural basis functions.
Proposes ReTAMamba, a method using reliability-aware temporal aggregation with Mamba for irregular clinical time series prediction, achieving significant AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.