School of Health and Wellbeing, College of Medical and Veterinary Sciences, University of Glasgow, Glasgow, UK.
Department of Psychiatry, University of Oxford, Oxford, UK.
Alzheimers Dement. 2023 Dec;19(12):5872-5884. doi: 10.1002/alz.13391. Epub 2023 Jul 26.
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).
应用模型在痴呆风险预测、诊断和预后中的使用将具有重大的公共卫生效益，特别是随着具有多组学健康数据的“深度表型”队列的出现。
本叙述性综述从痴呆风险预测、诊断区分、预后和进展等方面综合了对应用模型和数字健康技术的理解。与标准临床风险评分相比，机器学习方法在预测痴呆方面显示出具有更高的预测能力的证据，并且有可能将大量变量分解为相对较少的关键预测因子。
本综述重点关注新兴领域的关键领域，包括：强调更容易、更透明的数据共享和队列访问；将高通量生物标志物和电子健康记录数据纳入建模；并不仅仅预测痴呆，而是推进到次要结果，例如治疗反应和身体健康。
这种方法也将受益于远程数据测量的改进，无论是认知（例如在线）还是自然主义（例如基于手表的加速度计）。
