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arXiv Machine Learning

A reproducible and extensible framework for benchmarking competing risks survival models

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.

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arXiv Computation and Language
3d ago

Large Language Models are Approximate Survival Estimators

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...

By Juan M Zambrano Chaves, Peniel Argaw, Risa Ueno, Carlo Bifulco, Kristina Young, Rom Leidner, Tristan Naumann, Hoifung Poon
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arXiv Machine Learning
Jul 21

Tabular Foundation Models Can Do Survival Analysis

arXiv:2601. 22259v2 Announce Type: replace Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event of interest occurs.

By Da In Kim, Wei Siang Lai, Kelly W. Zhang
llms
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arXiv Machine Learning
Jun 2

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset

arXiv:2507. 07339v2 Announce Type: replace-cross Abstract: Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc.

By Yingtao Luo, Reza Skandari, Carlos Martinez, Arman Kilic, Rema Padman
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arXiv Machine Learning
Jun 4

SurvPFN: Towards Foundation Models for Survival Predictions

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.

By Samuel B\"ohm (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany), Lennart Purucker (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Frank Hutter (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Pascal Schlosser (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany)
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arXiv Machine Learning
Sep 14

DynSHAP: Towards Explainable Dynamic Survival Analysis

DynSHAP is a SHAP-based framework designed for dynamic survival analysis, extending marginal SHAP estimators to handle time–feature pairs as players in the Shapley game. It introduces Temporal DynSHAP, which models linear dependencies across time and uses conditional sampling to improve explanations. Experiments on synthetic data and two real-world clinical datasets show that Temporal DynSHAP more accurately recovers temporally dependent features and provides faithful attributions for two DSA architectures, enabling medical experts to identify which patient information influenced predictions and when.

By Nastasya Anokhina, Jonas J\"ur{\ss}, Pietro Li\`o
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