Identifying Comorbidity Patterns in People with and without Alzheimer’s Disease Using Latent Dirichlet Allocation
Early signs and symptoms of AD were more commonly clustered together in the AD cohort than in the comparison cohort. This study experimented with using natural language processing techniques for clustering patterns from an epidemiological study. From the computed clusters, it was possible to qualitatively identify multimorbidity that differentiates AD cases and controls.
Authors
- Billy Braithwaite
- Blair Rajamaki (ORCID: https://orcid.org/0000-0003-4792-9303)
- Sirpa A. Hartikainen (ORCID: https://orcid.org/0000-0002-4714-455X)
- Anna‐Maija Tolppanen (ORCID: https://orcid.org/0000-0001-9270-9268)
Institutions
- University of Eastern Finland (FI)
Publication Details
- Journal
- Journal of Alzheimer s Disease
- Published
- 2024-09-20
- DOI
- https://doi.org/10.3233/jad-240490
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Business Finland
- European Geriatric Medicine Society
- European Regional Development Fund