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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Dementia Prevention: optimizing the use of observational data for personal, clinical, and public health
Penny A Dacks1, Sandrine Andrieu2, Deborah Blacker3
1Alzheimer's Drug Discovery Foundation, New York, NY.
Abstract:
Worldwide, over 35 million people suffer from Alzheimer's disease and related dementias. This number is expected to triple over the next 40 years. How can we improve the evidence supporting strategies to reduce the rate of dementia in future generations? The risk of dementia is likely influenced by modifiable factors such as exercise, cognitive activity, and the clinical management of diabetes and hypertension. However, the quality of evidence is limited and it remains unclear whether specific interventions to reduce these modifiable risk factors can, in turn, reduce the risk of dementia. Although randomized controlled trials are the gold-standard for causality, the majority of evidence for long-term dementia prevention derives from, and will likely continue to derive from, observational studies. Although observational research has some unavoidable limitations, its utility for dementia prevention might be improved by, for example, better distinction between confirmatory and exploratory research, higher reporting standards, investment in effectiveness research enabled by increased data-pooling, and standardized exposure and outcome measures. Informed decision-making by the general public on low-risk health choices that could have broad potential benefits could be enabled by internet-based tools and decision-aids to communicate the evidence, its quality, and the estimated magnitude of effect.
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