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.
Graph-guided network for irregularly sampled multivari- ate time series.arXiv preprint arXiv:2110.05357
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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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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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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.