Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes

• URL: http://arxiv.org/abs/2509.13696v1
• Date: Wed, 17 Sep 2025 05:02:14 GMT
• Title: Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
• Authors: Iyadh Ben Cheikh Larbi, Ajay Madhavan Ravichandran, Aljoscha Burchardt, Roland Roller,
• Abstract summary: Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored.<n>In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly.
• Score: 0.6545884355643076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored. In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly. Our results show that this approach achieves performance on par with specialized multimodal systems while requiring less complexity and offering greater adaptability across tasks.

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