Summary of Leveraging Social Determinants Of Health in Alzheimer’s Research Using Llm-augmented Literature Mining and Knowledge Graphs, by Tianqi Shang et al.
Leveraging Social Determinants of Health in Alzheimer’s Research Using LLM-Augmented Literature Mining and Knowledge Graphs
by Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H. Moore, Marylyn D. Ritchie, Li Shen
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study presents a novel framework that leverages large language models and natural language processing techniques to integrate social determinants of health (SDoH) knowledge from literature with Alzheimer’s disease-related biological entities extracted from the PrimeKG knowledge graph. The framework uses graph neural networks for link prediction tasks, showing promise for enhancing knowledge discovery in AD and generalizing to other SDoH-related research areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using computers to help us understand how things like poverty or education level affect our risk of getting Alzheimer’s disease. They built a special tool that can look at lots of information from books and then connect it with what we already know about Alzheimer’s disease. This might help us learn more about why some people get Alzheimer’s and not others. |
Keywords
» Artificial intelligence » Knowledge graph » Natural language processing
