A novel computational analysis integrating social determinants information from EHR and literature with Alzheimer’s disease biological knowledge through large language models and knowledge graphs
This novel KG-based analysis enhances link prediction in AD-related biomedical networks by integrating SDoH and biological knowledge. Our findings highlight the potential interaction between social determinants and biological factors in AD, offering insights into more personalized and socially aware healthcare interventions.
Authors
- Joseph K. Lee
- Shu Yang (ORCID: https://orcid.org/0000-0002-8507-7191)
- Bojian Hou (ORCID: https://orcid.org/0000-0002-3894-4547)
- Jason H. Moore (ORCID: https://orcid.org/0000-0002-5015-1099)
- Tianqi Shang (ORCID: https://orcid.org/0009-0007-7147-0316)
- Duy Duong‐Tran (ORCID: https://orcid.org/0009-0009-4496-7575)
- Marylyn DeRiggi Ritchie (ORCID: https://orcid.org/0000-0002-1208-1720)
- Tianhua Zhai (ORCID: https://orcid.org/0009-0007-9712-9132)
- Elizabeth Mamourian (ORCID: https://orcid.org/0000-0001-8581-4887)
- Li Shen (ORCID: https://orcid.org/0000-0002-5443-0503)
- Weiqing He
- Jiayu Zhang (ORCID: https://orcid.org/0009-0003-9904-6892)
Institutions
- University of the Sciences (US)
- Cedars-Sinai Medical Center (US)
- United States Naval Academy (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Innovation in Aging
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1093/geroni/igaf102
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Institutes of Health