What is it about?

This review article provides an overview of research on Machine Learning (ML) utilizing Neuropsychiatric Symptoms and Alzheimer's disease biomarkers information in the context of brain aging. Studies included in this review provide convincing evidence for the potential application of ML in the context of brain aging.

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Why is it important?

Given the clinical significance of Neuropsychiatric Symptoms (NPS) in the context of brain aging, we argue that more studies on Machine Learning (ML), NPS, and Alzheimer's disease (AD) biomarkers need to be conducted. ML is a promising tool to identify persons at risk for progression to MCI or dementia. Therefore, the prediction model of MCI or dementia can be substantially improved by using ML in samples enriched by NPS. This may have implications for future AD prevention trials that target samples enriched with NPS.

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This page is a summary of: Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer’s Disease: A Literature Review from a Machine Learning Perspective, Journal of Alzheimer s Disease, April 2023, IOS Press,
DOI: 10.3233/jad-221261.
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