A General Paradigm for Fine-Tuning Large Language Models in Alzheimer’s Disease Diagnosis

Authors

Marcus Zhan
Kun Zhao
Guodong Liu
Haoteng Tang, The University of Texas Rio Grande Valley

Document Type

Conference Proceeding

Publication Date

5-28-2025

Abstract

Alzheimer's disease (AD), a complex neurodegenerative disorder, presents significant challenges for early and accurate diagnosis due to its multifactorial nature. This study introduces a novel approach to fine-tuning large language models (LLMs) for classifying AD-related dementia stages, using genetic and contextual demographic data. By harnessing the unique ability of LLMs to capture complex relationships in high-dimensional data, we developed a prompt structure that integrates genetic information, such as single nucleotide polymorphisms (SNPs), with patient-specific factors like age, sex, and clinical scores. Extensive experiments on the ADNI dataset demonstrate the superior performance of LLM-based methods. Our findings highlight the crucial role of high-quality prompts and carefully curated data in improving model accuracy. This research lays the groundwork for applying LLMs in precision medicine, providing a scalable and interpretable framework to address complex biomedical challenges, extending beyond AD.

Comments

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Recommended Citation

Zhan, Marcus, Kun Zhao, Guodong Liu, and Haoteng Tang. 2025. “A General Paradigm for Fine-Tuning Large Language Models in Alzheimer’s Disease Diagnosis”. Proceedings of the AAAI Symposium Series 5 (1):37-42. https://doi.org/10.1609/aaaiss.v5i1.35550

Publication Title

Proceedings of the AAAI Symposium Series

DOI

10.1609/aaaiss.v5i1.35550