Survival models show distinct interpolation behaviors driven by loss functions, so overparametrization does not reliably improve generalization as in regression or classification.
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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.
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
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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Understanding Overparametrization in Survival Models through Interpolation
Survival models show distinct interpolation behaviors driven by loss functions, so overparametrization does not reliably improve generalization as in regression or classification.
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Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
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.
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A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
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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.