An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
Explainable AI in healthcare: to explain, to predict, or to describe? Diagn Progn Res
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A-ROM delivers competitive MedMNIST performance via pretrained ViT metric spaces, a concept dictionary, and kNN without backpropagation or fine-tuning, framed as interpretable few-shot learning under the Platonic Representation Hypothesis.
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Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
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Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST
A-ROM delivers competitive MedMNIST performance via pretrained ViT metric spaces, a concept dictionary, and kNN without backpropagation or fine-tuning, framed as interpretable few-shot learning under the Platonic Representation Hypothesis.