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

arXiv 2509.13696 ↗First posted 17 Sep 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 17 Sep 2025.

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References · 9

  1. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  2. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  3. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 122
  4. 2023Large Language Models are Few-Shot Health Learnerscited by 118
  5. 2023Frozen Language Model Helps ECG Zero-Shot Learningcited by 62
  6. 2021Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integrationcited by 44
  7. 2019Using Clinical Notes with Time Series Data for ICU Managementcited by 38
  8. 2018Practical Text Classification With Large Pre-Trained Language Modelscited by 24
  9. 2021Literature-Augmented Clinical Outcome Predictioncited by 20

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