Machine learning to detect Alzheimer's disease with data on drugs and diagnoses
Machine learning applied to clinical data shows promise in predicting AD, with robust model performance across age and sex groups. The findings confirmed known risk factors, such as depression and vitamin B12 deficiency, while also identifying novel predictors that may guide future research. Clinically, this approach could enhance early detection and risk stratification, facilitating timely interventions and improving patient outcomes.
Authors
- Predrag Petrović (ORCID: https://orcid.org/0000-0002-5536-945X)
- Linus Jönsson (ORCID: https://orcid.org/0000-0001-5751-6292)
- Caroline Wachtler (ORCID: https://orcid.org/0000-0002-1317-7732)
- Miia Kivipelto (ORCID: https://orcid.org/0000-0003-0992-3875)
- Axel Carl Carlsson (ORCID: https://orcid.org/0000-0001-6113-0472)
- Nenad Bogdanović (ORCID: https://orcid.org/0000-0001-6092-2515)
- Johanna Wallensten
- Anna Olofsson (ORCID: https://orcid.org/0000-0002-7479-315X)
Institutions
- Danderyds sjukhus (SE)
- Karolinska Institutet (SE)
Publication Details
- Journal
- The Journal of Prevention of Alzheimer s Disease
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1016/j.tjpad.2025.100115
- Citations
- 3
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 4.41
Funders
- Stockholms Läns Landsting
- Vetenskapsrådet