Authors: Ziyuan Huang, Vishaldeep Kaur Sekhon, Ouyang Guo, Mark Newman, Roozbeh Sadeghian, Maria L. Vaida, Cynthia Jo, Doyle Ward, Vanni Bucci, John P. Haran
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.
Source: http://arxiv.org/abs/2501.08324v1
