@inproceedings{kruse-etal-2025-large,
title = "Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction",
author = "Kruse, Maya and
Hu, Shiyue and
Derby, Nicholas and
Wu, Yifu and
Stonbraker, Samantha and
Yao, Bingsheng and
Wang, Dakuo and
Goldberg, Elizabeth M. and
Gao, Yanjun",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1128/",
pages = "20715--20735",
ISBN = "979-8-89176-335-7",
abstract = "Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-engineering existing tasks, including discharge summarization and diagnosis prediction from two publicly available EHR datasets. Our results indicate that long context windows improve input integration but do not consistently enhance clinical reasoning, and LLMs are still struggling with temporal progression and rare disease prediction. While RAG shows improvements in hallucination in some cases, it does not fully address these limitations. Our work fills the gap in long clinical text summarization, establishing a foundation for evaluating LLMs with multi-modal data and temporal reasoning."
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<abstract>Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-engineering existing tasks, including discharge summarization and diagnosis prediction from two publicly available EHR datasets. Our results indicate that long context windows improve input integration but do not consistently enhance clinical reasoning, and LLMs are still struggling with temporal progression and rare disease prediction. While RAG shows improvements in hallucination in some cases, it does not fully address these limitations. Our work fills the gap in long clinical text summarization, establishing a foundation for evaluating LLMs with multi-modal data and temporal reasoning.</abstract>
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%0 Conference Proceedings
%T Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction
%A Kruse, Maya
%A Hu, Shiyue
%A Derby, Nicholas
%A Wu, Yifu
%A Stonbraker, Samantha
%A Yao, Bingsheng
%A Wang, Dakuo
%A Goldberg, Elizabeth M.
%A Gao, Yanjun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kruse-etal-2025-large
%X Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-engineering existing tasks, including discharge summarization and diagnosis prediction from two publicly available EHR datasets. Our results indicate that long context windows improve input integration but do not consistently enhance clinical reasoning, and LLMs are still struggling with temporal progression and rare disease prediction. While RAG shows improvements in hallucination in some cases, it does not fully address these limitations. Our work fills the gap in long clinical text summarization, establishing a foundation for evaluating LLMs with multi-modal data and temporal reasoning.
%U https://aclanthology.org/2025.findings-emnlp.1128/
%P 20715-20735
Markdown (Informal)
[Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction](https://aclanthology.org/2025.findings-emnlp.1128/) (Kruse et al., Findings 2025)
ACL
- Maya Kruse, Shiyue Hu, Nicholas Derby, Yifu Wu, Samantha Stonbraker, Bingsheng Yao, Dakuo Wang, Elizabeth M. Goldberg, and Yanjun Gao. 2025. Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20715–20735, Suzhou, China. Association for Computational Linguistics.