ADAM: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration

TL;DR

ADAM is a multi-agent LLM framework that integrates microbiome, clinical, and external data to improve Alzheimer's disease classification and understanding, showing superior robustness over traditional models.

Contribution

The paper introduces ADAM, a novel multi-agent reasoning LLM that effectively combines multimodal data for Alzheimer's diagnosis, outperforming existing models in accuracy and robustness.

Findings

ADAM achieves higher mean F1 scores than XGBoost.

ADAM demonstrates reduced variance and increased robustness.

The framework successfully integrates diverse data sources for AD analysis.

Abstract

Alzheimer's Disease Analysis Model (ADAM) is a multi-agent reasoning large language model (LLM) framework designed to integrate and analyze multimodal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and classification of Alzheimer's disease (AD). By leveraging the agentic system with LLM, ADAM produces insights from diverse data sources and contextualizes the findings with literature-driven evidence. A comparative evaluation with XGBoost revealed a significantly improved mean F1 score and significantly reduced variance for ADAM, highlighting its robustness and consistency, particularly when utilizing human biological data. Although currently tailored for binary classification tasks with two data modalities, future iterations will aim to incorporate additional data types, such as neuroimaging and peripheral biomarkers, and expand them to predict disease progression, thereby broadening ADAM's scalability and applicability in AD research and diagnostic applications.