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

TL;DR

This paper introduces an automated framework that combines large language models and knowledge graphs to mine social determinants of health from literature and explore their links to Alzheimer's disease, aiding in knowledge discovery.

Contribution

The study presents a novel LLM-augmented literature mining and knowledge graph integration approach for SDoH in Alzheimer's research, enabling automated knowledge extraction and link prediction.

Findings

Effective extraction of SDoH knowledge from literature.

Successful integration with AD-related biological entities.

Enhanced potential for knowledge discovery in AD research.

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