KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli
Continuous and discrete-time survival prediction with neural networks.Lifetime Data Analysis, 27(4):710–736, October 2021
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Adapting tabular foundation models with an MTLR survival head produces competitive or superior C-index scores on MIMIC-IV (0.856) and eICU (0.797) compared to DeepSurv and zero-shot baselines.
citing papers explorer
-
KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis
KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli