MedicalBench is a benchmark for implicit medical concept extraction and sentence-level evidence retrieval built from MIMIC-IV discharge summaries with human verification to test LLM reasoning on unstated medical ideas.
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GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
The UK Co-Benefits Atlas design process yields a conceptual framework of five driving forces—data, people, stories, context, and the atlas itself—that shape visualization atlas creation at different stages.
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
UMAP embeddings of EHR data for five ED chief complaints yield 2-6 clusters with moderate stability (ARI 0.35-0.74), presented as evidence for data-driven patient phenotypes.
citing papers explorer
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MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction
MedicalBench is a benchmark for implicit medical concept extraction and sentence-level evidence retrieval built from MIMIC-IV discharge summaries with human verification to test LLM reasoning on unstated medical ideas.
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GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI
GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
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Designing a Visualization Atlas: Lessons & Reflections from The UK Co-Benefits Atlas for Climate Mitigation
The UK Co-Benefits Atlas design process yields a conceptual framework of five driving forces—data, people, stories, context, and the atlas itself—that shape visualization atlas creation at different stages.
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MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
MetaboNet is a consolidated dataset of 3135 subjects with 1228 patient-years of CGM and insulin pump data for Type 1 Diabetes research.
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Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
UMAP embeddings of EHR data for five ED chief complaints yield 2-6 clusters with moderate stability (ARI 0.35-0.74), presented as evidence for data-driven patient phenotypes.