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

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.