Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
- URL: http://arxiv.org/abs/2410.09080v1
- Date: Fri, 4 Oct 2024 21:39:30 GMT
- Title: Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
- Authors: Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H. Moore, Marylyn D. Ritchie, Li Shen,
- Abstract summary: Growing evidence suggests that social determinants of health (SDoH) affect individuals' risks of developing Alzheimer's disease (AD) and related dementias.
This study presents a novel, automated framework to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities.
Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas.
- Score: 33.755845172595365
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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
Related papers
- Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare [47.23120247002356]
Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored.
This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG.
arXiv Detail & Related papers (2024-11-29T20:35:01Z) - Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.
Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
arXiv Detail & Related papers (2024-10-06T18:46:28Z) - A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights [0.4915744683251151]
Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors.
To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD.
The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder.
arXiv Detail & Related papers (2024-09-19T15:50:22Z) - Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape.
RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
arXiv Detail & Related papers (2024-07-17T07:44:18Z) - myAURA: Personalized health library for epilepsy management via knowledge graph sparsification and visualization [4.25313339005458]
myAURA is an application designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self-management.
MyAURA rests on the federation of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records.
arXiv Detail & Related papers (2024-05-08T17:24:24Z) - Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge [2.2814097119704058]
Large language models (LLMs) are transforming the way information is retrieved with vast amounts of knowledge being summarized and presented.
LLMs are prone to highlight the most frequently seen pieces of information from the training set and to neglect the rare ones.
We introduce a novel information-retrieval method that leverages a knowledge graph to downsample these clusters and mitigate the information overload problem.
arXiv Detail & Related papers (2024-02-19T18:31:11Z) - Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue
Generation [150.52617238140868]
We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones.
We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
arXiv Detail & Related papers (2020-12-22T13:20:23Z) - A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease
Progression with MEG Brain Networks [59.15734147867412]
Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression.
We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G)
We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
arXiv Detail & Related papers (2020-05-08T02:29:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.