痴呆
仿形(计算机编程)
临床试验
重新调整用途
风险分析(工程)
优先次序
医学
疾病
人工智能
计算机科学
管理科学
病理
工程类
操作系统
废物管理
作者
Danielle Newby,Vasiliki Orgeta,Charles R. Marshall,Ilianna Lourida,Christopher P. Albertyn,Stefano Tamburin,Vanessa Raymont,Michele Veldsman,Ivan Koychev,Sarah Bauermeister,David C. Weisman,Isabelle F Foote,Magda Bucholc,Anja Leist,Eugene Tang,Xin You Tai,David J. Llewellyn,Janice M Ranson
摘要
Abstract INTRODUCTION A 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. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk‐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. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. Highlights Artificial 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.
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