Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.
Predicting future disorders via temporal knowledge graphs and medicalontologies.IEEEJournalofBiomedicaland Health Informatics, 28(7):4238–4248
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DATGR updates co-occurrence edge weights via a logistic rule that mixes prior weight, current strength, change, and embedding drift, raising AUROC ~0.066 over a static baseline while holding AUPRC steady.
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ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.
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Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
DATGR updates co-occurrence edge weights via a logistic rule that mixes prior weight, current strength, change, and embedding drift, raising AUROC ~0.066 over a static baseline while holding AUPRC steady.