The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, 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 focuses 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 modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, 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).

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This post is Copyright: Donald M. Lyall,
Andrey Kormilitzin,
Claire Lancaster,
Jose Sousa,
Fanny Petermann‐Rocha,
Christopher Buckley,
Eric L. Harshfield,
Matthew H. Iveson,
Christopher R. Madan,
Ríona McArdle,
Danielle Newby,
Vasiliki Orgeta,
Eugene Tang,
Stefano Tamburin,
Lokendra S. Thakur,
Ilianna Lourida,
The Deep Dementia Phenotyping (DEMON) Network,
David J. Llewellyn,
Janice M. Ranson | July 27, 2023

Wiley: Alzheimer’s & Dementia: Table of Contents