Language Models for Longitudinal Clinical Prediction
By: Tananun Songdechakraiwut, Michael Lutz
Potential Business Impact:
Helps doctors predict diseases early from patient notes.
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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