most citedSurvival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks

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cs.LG2026

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov +5

The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient st…

cs.LG2026★ 1 cited

Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks

Dmitrii Seletkov, Paul Hager, Georgios Kaissis +3

Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular cov…