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Lookup NU author(s): Claire Lancaster, Dr Chris Buckley, Dr Ríona McArdle, Dr Eugene TangORCiD, Dr Ilianna Lourida
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The use of applied modelling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefit, particularly as ‘deep phenotyping’ cohorts with multi-omics health data become available. This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk-scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. This review focusses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access, integration of high-throughput biomarker and electronic health record data into modelling, and progressing beyond primary prediction of dementia to secondary outcomes e.g., treatment response and physical health. Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g. online), or naturalistic (e.g. watch-based accelerometry).
Author(s): Lyall D, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, Harshfield E, Iveson M, Madan C, Mc Ardle R, Newby D, Orgeta V, Tang E, Tamburin S, Thakur L, Lourida I, Deep Dementia Phenotyping Network The, Llewellyn D, Ranson J
Publication type: Review
Publication status: Published
Journal: Alzheimer's & Dementia: The Journal of the Alzheimer's Association
Year: 2023
Volume: 19
Issue: 12
Pages: 5872-5884
Print publication date: 01/12/2023
Online publication date: 26/07/2023
Acceptance date: 26/05/2023
ISSN (electronic): 1552-5279
URL: https://doi.org/10.1002/alz.13391
DOI: 10.1002/alz.13391
