Technology Category
Multiagent Systems: Adversarial AgentsMachine Learning: Large Multimodal Models (LMMs)Cognitive Modeling & Cognitive Systems: Agent Architectures
ADAM-1: AI and Bioinformatics for Alzheimer's Detection and Microbiome-Clinical Data Integrations
🤖 AI Summary
Early diagnosis of Alzheimer’s disease (AD) remains challenging, particularly under small-sample conditions where conventional models exhibit poor robustness. To address this, we propose a Retrieval-Augmented Generation (RAG)-driven multi-agent large language model framework that uniquely integrates gut microbiome data, clinical electronic health records, and structured biomedical knowledge bases. Leveraging coordinated agent roles—retrieval, reasoning, and verification—coupled with dynamic knowledge enhancement, the framework enables interpretable fusion of heterogeneous multimodal data. In AD detection, our method achieves F1-score parity with XGBoost while significantly reducing prediction variance (↓62%), demonstrating superior stability and consistent generalization on small-scale laboratory datasets. The core contribution lies in deeply embedding RAG into a multi-agent collaborative paradigm, establishing a novel small-data modeling framework for neurodegenerative diseases.
Technology Category
Application Category
📝 Abstract
The Alzheimer's Disease Analysis Model Generation 1 (ADAM) is a multi-agent large language model (LLM) framework designed to integrate and analyze multi-modal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and detection of Alzheimer's disease (AD). By leveraging retrieval-augmented generation (RAG) techniques along with its multi-agent architecture, ADAM-1 synthesizes insights from diverse data sources and contextualizes findings using literature-driven evidence. Comparative evaluation against XGBoost revealed similar mean F1 scores but significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly in small laboratory datasets. While currently tailored for binary classification tasks, future iterations aim to incorporate additional data modalities, such as neuroimaging and biomarkers, to broaden the scalability and applicability for Alzheimer's research and diagnostics.
Problem
Research questions and friction points this paper is trying to address.
Alzheimer's Disease
Early Diagnosis
Multisource Information Fusion
Innovation
Methods, ideas, or system contributions that make the work stand out.
Alzheimer's Disease Detection
Information Fusion
Multi-source Data Analysis
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