Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs

TL;DR

This paper introduces TAP-GPT, a novel framework that adapts a multimodal tabular LLM for early Alzheimer's diagnosis using small biomarker datasets, achieving superior predictive performance.

Contribution

It is the first to apply large language models to tabular biomarker data for Alzheimer's diagnosis, combining few-shot learning and parameter-efficient fine-tuning.

Findings

Outperforms other LLMs and models in AD prediction accuracy.

Effectively uses few-shot prompts with small datasets.

Demonstrates potential for LLMs in biomedical tabular data analysis.

Abstract

Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk factors, cognitive tests, and cerebrospinal fluid proteins) typically represented in a tabular format. With flexible few-shot reasoning, multimodal integration, and natural-language-based interpretability, large language models (LLMs) offer unprecedented opportunities for prediction with structured biomedical data. We propose a novel framework called TAP-GPT, Tabular Alzheimer's Prediction GPT, that adapts TableGPT2, a multimodal tabular-specialized LLM originally developed for business intelligence tasks, for AD diagnosis using structured biomarker data with small sample sizes. Our approach constructs few-shot tabular prompts using in-context learning examples from structured biomedical data and finetunes TableGPT2 using the parameter-efficient qLoRA adaption for a clinical binary classification task of AD or cognitively normal (CN). The TAP-GPT framework harnesses the powerful tabular understanding ability of TableGPT2 and the encoded prior knowledge of LLMs to outperform more advanced general-purpose LLMs and a tabular foundation model (TFM) developed for prediction tasks. To our knowledge, this is the first application of LLMs to the prediction task using tabular biomarker data, paving the way for future LLM-driven multi-agent frameworks in biomedical informatics.