arXiv:2607. 16802v1 Announce Type: new Abstract: Deep survival models are evaluated almost exclusively by the concordance index (C-index), yet they are commonly trained using likelihood objectives such as the Cox partial likelihood, discrete-time negative log-likelihood, and DeepHit likelihood.
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
arXiv:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.
arXiv:2606. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.
arXiv:2609.38181v1 Announce Type: new Abstract: Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic...
arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.
arXiv:2606. 08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints.
The paper explores how to adapt tabular foundation models (TabFMs) for censored time‑to‑event prediction by linking them with CoxPH and DeepHit and revising training procedures. It evaluates zero‑shot, classification‑based fine‑tuning, and survival‑head adaptations across 74 single‑risk and 4 competing‑risk datasets, finding that zero‑shot works best on small datasets while supervised adaptation excels as data grows. The study shows that the choice of adaptation interface and data regime critically influences TabFM transfer performance.
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
arXiv:2608.29419v1 Announce Type: cross Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over s...
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
The paper introduces a multimodal dataset for survival prediction in resected pancreatic ductal adenocarcinoma, comprising 302 patients, 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall survival outcomes. The authors evaluated fourteen survival‑prediction models, finding that a Ridge Cox regression on numeric clinicopathological variables achieved the highest concordance (≈0.65), while multimodal fusion of image and molecular data reached 0.619. These benchmarks provide a foundation for future research and external validation using this pancreas‑specific dataset.
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.