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Artificial intelligence for dementia-Applied models and digital health.

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Lyall, Donald M 
Kormilitzin, Andrey 
Lancaster, Claire 
Sousa, Jose 
Petermann-Rocha, Fanny 


INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).



AI, ML, applied models, artificial intelligence, dementia, digital health, machine learning

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Alzheimers Dement

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British Heart Foundation (RE/18/1/34212)
MRC (via University College London (UCL)) (MR/X005674/1)
Medical Research Council (MR/W014386/1)
Medical Research Council (MR/X005674/1)
This paper was the product of a DEMON Network state of the science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer’s Research UK. AK is supported in part by the NIHR AI Award (AI_AWARD02183). CL is funded by Alzheimer’s Society and Alzheimer’s Research UK. ELH is supported by the Cambridge British Heart Foundation Centre of Research Excellence (RE/18/1/34212). MHI is supported by the UK Research and Innovation-funded DATAMIND project (MR/W014386/1). ET (National Institute for Health Research (NIHR) Clinical Lectureship) is funded by the NIHR. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. JMR and DJL are supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654).