Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective

Document Type

Article

Abstract

There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Medical Subject Headings

Humans; Alzheimer Disease (diagnosis, psychology); Cognitive Dysfunction (diagnosis, psychology); Cognition; Machine Learning; Biomarkers; Disease Progression

Publication Date

1-1-2023

Publication Title

Journal of Alzheimer's disease : JAD

E-ISSN

1875-8908

Volume

92

Issue

4

First Page

1131

Last Page

1146

PubMed ID

36872783

Digital Object Identifier (DOI)

10.3233/JAD-221261

Link to Full Text

Share

COinS