Leveraging multi‐modal data for individualised risk prediction for dementia
This work provides novel insights into specific proteins crucial for assessing individualised risk prediction for dementia and early diagnosis. The study confirmed machine learning as a viable method to help realise the benefits of preclinical dementia risk prediction. It will also shed light into differences per dementia sub-type and how risk profiles differ across ethnic groups and gender.
Authors
- Petroula Proitsi (ORCID: https://orcid.org/0000-0002-2553-6974)
- Yue Liu (ORCID: https://orcid.org/0009-0007-8136-6627)
- Oleg B. Blyuss (ORCID: https://orcid.org/0000-0002-0194-6389)
- Sheena Waters (ORCID: https://orcid.org/0000-0001-7241-2272)
- Charles R. Marshall (ORCID: https://orcid.org/0000-0002-8227-2354)
- Sara Calhas
Institutions
- Queen Mary University of London (GB)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_102784
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00