Do LLMs Have a Sense of Time? Zero-Shot Survival Curve Prediction with Frontier Language Models

Emma Chen, Grace Chang Yuan, Christine Yang Zhou, David A Kim, Pranav Rajpurkar
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:336-393, 2026.

Abstract

Survival curve prediction is an underexplored formulation for time-to-event prediction with large language models (LLMs). We benchmark four frontier LLMs on zero-shot survival curve prediction for emergency department (ED) revisit and hospital readmission, comparing against regression and ordinal classification as alternative prediction formulations. Survival prediction is the top-performing formulation for every model on readmission prediction and three of four on ED revisit prediction, with Harrell’s C-index ranging from 0.703–0.732 and 0.713–0.802 respectively. Frontier LLMs can produce largely well-formed survival curves zero-shot, with all predictions being valid probabilities and monotonicity holds on 99–100% of predicted curves without post-hoc correction; the initial value condition $S(0)=1$ is satisfied exactly by two of four models on both tasks and by a third model on readmission, and approximately otherwise. Beyond structural validity, the best LLMs approach a supervised Random Survival Forest baseline (C-index 0.732 vs. 0.741) on readmission prediction with no training data. Compared to direct binary prompting, survival-derived probabilities are substantially better calibrated at every horizon tested, with ECE reductions of up to 7.5$\times$ at short horizons. Structured multi-agent deliberation achieved C-indices of 0.734 for readmission and 0.790 for ED revisit; it significantly improved on the weakest baseline for ED Revisit ($\Delta=+0.031$, two-sided $p=0.049$ for GPT-5) but did not consistently outperform the strongest individual model. These results establish survival curve prediction as the preferred formulation for LLM-based clinical time-to-event prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-chen26b, title = {Do LLMs Have a Sense of Time? Zero-Shot Survival Curve Prediction with Frontier Language Models}, author = {Chen, Emma and Yuan, Grace Chang and Zhou, Christine Yang and Kim, David A and Rajpurkar, Pranav}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {336--393}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/chen26b/chen26b.pdf}, url = {https://proceedings.mlr.press/v340/chen26b.html}, abstract = {Survival curve prediction is an underexplored formulation for time-to-event prediction with large language models (LLMs). We benchmark four frontier LLMs on zero-shot survival curve prediction for emergency department (ED) revisit and hospital readmission, comparing against regression and ordinal classification as alternative prediction formulations. Survival prediction is the top-performing formulation for every model on readmission prediction and three of four on ED revisit prediction, with Harrell’s C-index ranging from 0.703–0.732 and 0.713–0.802 respectively. Frontier LLMs can produce largely well-formed survival curves zero-shot, with all predictions being valid probabilities and monotonicity holds on 99–100% of predicted curves without post-hoc correction; the initial value condition $S(0)=1$ is satisfied exactly by two of four models on both tasks and by a third model on readmission, and approximately otherwise. Beyond structural validity, the best LLMs approach a supervised Random Survival Forest baseline (C-index 0.732 vs. 0.741) on readmission prediction with no training data. Compared to direct binary prompting, survival-derived probabilities are substantially better calibrated at every horizon tested, with ECE reductions of up to 7.5$\times$ at short horizons. Structured multi-agent deliberation achieved C-indices of 0.734 for readmission and 0.790 for ED revisit; it significantly improved on the weakest baseline for ED Revisit ($\Delta=+0.031$, two-sided $p=0.049$ for GPT-5) but did not consistently outperform the strongest individual model. These results establish survival curve prediction as the preferred formulation for LLM-based clinical time-to-event prediction.} }
Endnote
%0 Conference Paper %T Do LLMs Have a Sense of Time? Zero-Shot Survival Curve Prediction with Frontier Language Models %A Emma Chen %A Grace Chang Yuan %A Christine Yang Zhou %A David A Kim %A Pranav Rajpurkar %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-chen26b %I PMLR %P 336--393 %U https://proceedings.mlr.press/v340/chen26b.html %V 340 %X Survival curve prediction is an underexplored formulation for time-to-event prediction with large language models (LLMs). We benchmark four frontier LLMs on zero-shot survival curve prediction for emergency department (ED) revisit and hospital readmission, comparing against regression and ordinal classification as alternative prediction formulations. Survival prediction is the top-performing formulation for every model on readmission prediction and three of four on ED revisit prediction, with Harrell’s C-index ranging from 0.703–0.732 and 0.713–0.802 respectively. Frontier LLMs can produce largely well-formed survival curves zero-shot, with all predictions being valid probabilities and monotonicity holds on 99–100% of predicted curves without post-hoc correction; the initial value condition $S(0)=1$ is satisfied exactly by two of four models on both tasks and by a third model on readmission, and approximately otherwise. Beyond structural validity, the best LLMs approach a supervised Random Survival Forest baseline (C-index 0.732 vs. 0.741) on readmission prediction with no training data. Compared to direct binary prompting, survival-derived probabilities are substantially better calibrated at every horizon tested, with ECE reductions of up to 7.5$\times$ at short horizons. Structured multi-agent deliberation achieved C-indices of 0.734 for readmission and 0.790 for ED revisit; it significantly improved on the weakest baseline for ED Revisit ($\Delta=+0.031$, two-sided $p=0.049$ for GPT-5) but did not consistently outperform the strongest individual model. These results establish survival curve prediction as the preferred formulation for LLM-based clinical time-to-event prediction.
APA
Chen, E., Yuan, G.C., Zhou, C.Y., Kim, D.A. & Rajpurkar, P.. (2026). Do LLMs Have a Sense of Time? Zero-Shot Survival Curve Prediction with Frontier Language Models. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:336-393 Available from https://proceedings.mlr.press/v340/chen26b.html.

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