Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction

• URL: http://arxiv.org/abs/2501.18724v3
• Date: Thu, 04 Sep 2025 19:36:17 GMT
• Title: Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction
• Authors: Maya Kruse, Shiyue Hu, Nicholas Derby, Yifu Wu, Samantha Stonbraker, Bingsheng Yao, Dakuo Wang, Elizabeth Goldberg, Yanjun Gao,
• Abstract summary: Large language models (LLMs) have shown potential in clinical text summarization.<n>Recent advances in large language models (LLMs) have shown potential in clinical text summarization.<n>This study systematically evaluates several state-of-the-art open-source LLMs.
• Score: 27.00269079168807
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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