Language Models for Longitudinal Clinical Prediction
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909873292181504 |
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| author | Songdechakraiwut, Tananun Lutz, Michael |
| author_facet | Songdechakraiwut, Tananun Lutz, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language Models for Longitudinal Clinical Prediction Songdechakraiwut, Tananun Lutz, Michael Computation and Language 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. |
| title | Language Models for Longitudinal Clinical Prediction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.23884 |