ImProNCDE adds Residual Impulse Calibration and Prototype-guided Trajectory Stabilizer to NCDEs to better model abrupt pathological changes and stabilize long-horizon predictions on irregular longitudinal ophthalmic data.
Graph-guided network for irregularly sampled multivari- ate time series.arXiv preprint arXiv:2110.05357
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HealthPoint represents clinical events as points in a 4D space (content, time, modality, case) and applies low-rank relational attention to achieve state-of-the-art mortality prediction from multi-level incomplete multimodal EHRs.
Under-Cali is an uncertainty-driven dual-expert calibration framework for online adaptation in irregular multivariate time series forecasting that freezes the base model.
ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
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.
citing papers explorer
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ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction
ImProNCDE adds Residual Impulse Calibration and Prototype-guided Trajectory Stabilizer to NCDEs to better model abrupt pathological changes and stabilize long-horizon predictions on irregular longitudinal ophthalmic data.
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A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs
HealthPoint represents clinical events as points in a 4D space (content, time, modality, case) and applies low-rank relational attention to achieve state-of-the-art mortality prediction from multi-level incomplete multimodal EHRs.
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Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
Under-Cali is an uncertainty-driven dual-expert calibration framework for online adaptation in irregular multivariate time series forecasting that freezes the base model.
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ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
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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.