GRDM jointly generates relational database tables via graph-conditional diffusion without table ordering, outperforming autoregressive baselines on multi-hop correlations and single-table fidelity across six real RDBs.
Mimic-iii, a freely accessible critical care database.Scientific data, 3(1):1–9
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ClinSeekAgent automates active multimodal evidence seeking for clinical reasoning, improving LLM performance on raw EHR and CXR tasks while enabling distillation into smaller models.
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
Systematic review of 134 studies consolidates synthetic tabular health data evaluation methods into taxonomies and provides guidelines to address challenges like inconsistent metrics and limited reproducibility.
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
citing papers explorer
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Joint Relational Database Generation via Graph-Conditional Diffusion Models
GRDM jointly generates relational database tables via graph-conditional diffusion without table ordering, outperforming autoregressive baselines on multi-hop correlations and single-table fidelity across six real RDBs.
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ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
ClinSeekAgent automates active multimodal evidence seeking for clinical reasoning, improving LLM performance on raw EHR and CXR tasks while enabling distillation into smaller models.
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Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
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Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Systematic review of 134 studies consolidates synthetic tabular health data evaluation methods into taxonomies and provides guidelines to address challenges like inconsistent metrics and limited reproducibility.
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Comparative Analysis of Large Language Models in Healthcare
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
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