Identifying dementia neuropathology using low‐burden clinical data
Clinical data are useful for neuropathology screening in future clinical trials. Novel application of semi-supervised learning for identifying neuropathology. Clustering model found groups with highly different neuropathology prevalence. Low-burden data can provide relatively accurate predictions of pathology load. Higher-burden, longitudinal data are most helpful for predicting vascular lesions.
Authors
- Babak Shahbaba (ORCID: https://orcid.org/0000-0002-8102-1609)
- Craig E.L. Stark (ORCID: https://orcid.org/0000-0002-9334-8502)
- Yueqi Ren (ORCID: https://orcid.org/0000-0003-2936-6009)
Institutions
- University of California, Irvine (US)
- University of California, Irvine Medical Center (US)
- Irvine University (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/alz.70539
- Citations
- 2
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- National Alzheimer's Coordinating Center
- National Institutes of Health
- National Institute on Aging
- National Institute of Mental Health
- National Cancer Institute