Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs

2024-10-04

arXiv.org

AI Summary

The causal mechanisms linking social determinants of health (SDoH) to Alzheimer’s disease (AD), as well as the scarcity of integrated, high-quality SDoH-AD data, impede mechanistic understanding and translational research. Method: We propose the first large language model (LLM)-driven knowledge fusion framework for SDoH–AD integration. Leveraging domain-adapted literature mining and LLM-assisted co-extraction, we systematically unify SDoH concepts with AD-related biological entities, constructing the first SDoH-augmented AD knowledge graph—built upon PrimeKG. We further embed SDoH systematically into the neurodegenerative disease knowledge graph and apply graph neural networks (GNNs) for link prediction. Contribution/Results: Our approach achieves an AUC of 0.89 in link prediction. The framework is open-source and demonstrates cross-disease generalizability for SDoH mechanism discovery. It establishes a novel paradigm for social-factor-informed precision neuroscience.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relationships remain largely unclear, mainly due to difficulties in collecting relevant information. This study presents a novel, automated framework that leverages recent advancements of large language model (LLM) and natural language processing techniques to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities extracted from the general-purpose knowledge graph PrimeKG. Utilizing graph neural networks, we performed link prediction tasks to evaluate the resultant SDoH-augmented knowledge graph. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas, offering a new tool for exploring the impact of social determinants on health outcomes. Our code is available at: https://github.com/hwq0726/SDoHenPKG

Problem

• Investigates unclear links between social determinants and Alzheimer's disease.
• Develops LLM-based framework to mine SDoH-AD relationships from literature.
• Integrates SDoH data with biological knowledge graphs for enhanced discovery.

Innovation

• LLM-augmented literature mining for SDoH knowledge
• Integration with AD-related entities from PrimeKG
• Graph neural networks for link prediction tasks