Language Models for Longitudinal Clinical Prediction

Tananun Songdechakraiwut, Michael Lutz
10/27/2025
cs.CL

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Abstract

We explore a lightweight framework that adapts frozen large language models to analyze longitudinal clinical data. The approach integrates patient history and context within the language model space to generate accurate forecasts without model fine-tuning. Applied to neuropsychological assessments, it achieves accurate and reliable performance even with minimal training data, showing promise for early-stage Alzheimer's monitoring.

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Cite this paper

@article{songdechakraiwut2025language,
  title  = {Language Models for Longitudinal Clinical Prediction},
  author = {Tananun Songdechakraiwut and Michael Lutz},
  year   = {2025},
  eprint = {2510.23884},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.23884v1}
}

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