New unsupervised method adapts the multivariate logrank statistic into a differentiable loss for training any neural network on any data modality to discover prognostically distinct patient clusters, demonstrated on myeloma lab data and lung cancer CT images with post-hoc explainability.
& Silva, E
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A new functional clustering framework for survival data that smooths log-hazard trajectories with B-splines, applies FPCA, and clusters on the scores to group by temporal risk dynamics.
citing papers explorer
-
Functional Clustering of Survival Data via Smoothed Log-Hazard Trajectories: A Risk-Dynamics Perspective
A new functional clustering framework for survival data that smooths log-hazard trajectories with B-splines, applies FPCA, and clusters on the scores to group by temporal risk dynamics.