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A multidimensional prediction-context framework that complements a calibrated mortality estimate with model behavior, data availability, recent physiology, and model attribution is developed, extending interpretation beyond risk scores and feature rankings alone.
The proposed GPT2-based table-to-text framework provides a practical and clinically interpretable approach for disease prediction from limited structured healthcare data and demonstrates strong potential for early risk detection, transparent clinical decision support, and reliable deployment in real-world low-resource...
CoMedBench is introduced, a reproducible benchmark that evaluates a family of generators under a common clinical-validity framework and one shared training and evaluation engine, spanning static tabular and temporal downstream tasks on established critical-care datasets.
Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key driver...
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