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
Austin, Frank E
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Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
citing papers explorer
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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
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A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.