Artificial intelligence for dementia prevention

被引:4
作者
Newby, Danielle [1 ]
Orgeta, Vasiliki [2 ]
Marshall, Charles R. [3 ,4 ]
Lourida, Ilianna [5 ,6 ]
Albertyn, Christopher P. [7 ]
Tamburin, Stefano [8 ]
Raymont, Vanessa [1 ]
Veldsman, Michele [9 ,10 ]
Koychev, Ivan [1 ]
Bauermeister, Sarah [1 ]
Weisman, David [11 ]
Foote, Isabelle F. [3 ,12 ]
Bucholc, Magda [13 ]
Leist, Anja K. [14 ]
Tang, Eugene Y. H. [5 ]
Tai, Xin You [15 ,16 ]
Llewellyn, David J. [6 ,17 ]
Ranson, Janice M. [6 ]
机构
[1] Univ Oxford, Warneford Hosp, Dept Psychiat, Oxford, England
[2] UCL, Div Psychiat, London, England
[3] Queen Mary Univ London, Barts & London Sch Med & Dent, Wolfson Inst Populat Hlth, Prevent Neurol Unit, London, England
[4] Royal London Hosp, Dept Neurol, London, England
[5] Newcastle Univ, Populat Hlth Sci Inst, Newcastle Upon Tyne, England
[6] Univ Exeter, Med Sch, Exeter, England
[7] Kings Coll London, Dept Old Age Psychiat, Inst Psychiat Psychol & Neurosci, London, England
[8] Univ Verona, Dept Neurosci Biomed & Movement Sci, Verona, Italy
[9] Univ Oxford, Wellcome Ctr Integrat Neuroimaging, Oxford, England
[10] Univ Oxford, Dept Expt Psychol, Oxford, England
[11] Abington Neurol Associates, Abington, PA USA
[12] Univ Colorado Boulder, Inst Behav Genet, Boulder, CO USA
[13] Ulster Univ, Sch Comp Engn & Intelligent Syst, Cognit Analyt Res Lab, Derry, Ireland
[14] Univ Luxembourg, Inst Res Socioecon Inequal IRSEI, Dept Social Sci, Esch Sur Alzette, Luxembourg
[15] Univ Oxford, Nuffield Dept Clin Neurosci, Oxford, England
[16] Oxford Univ Hosp Trust, John Radcliffe Hosp, Div Clin Neurol, Oxford, England
[17] Alan Turing Inst, London, England
基金
英国医学研究理事会; 美国国家卫生研究院; 英国工程与自然科学研究理事会; 欧洲研究理事会; 英国经济与社会研究理事会;
关键词
artificial intelligence; dementia; machine learning; prevention; risk prediction; ALZHEIMERS-DISEASE; RISK-FACTORS; PREDICTION; POPULATION; INTERVENTION; SCORE; CLASSIFICATION; TRAJECTORIES; ASSOCIATIONS; RECRUITMENT;
D O I
10.1002/alz.13463
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
INTRODUCTIONA wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.METHODSML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.RESULTSRisk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.DISCUSSIONML is not yet widely used but has considerable potential to enhance precision in dementia prevention.HighlightsArtificial intelligence (AI) is not widely used in the dementia prevention field.Risk-profiling tools are not used in clinical practice.Causal insights are needed to understand risk factors over the lifespan.AI will help personalize risk-management tools for dementia prevention.AI could target specific patient groups that will benefit most for clinical trials.
引用
收藏
页码:5952 / 5969
页数:18
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