Affiliations: [a] School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| [b] ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| [c]
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| [d] Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| [e] Biodesign Institute, Arizona State University, Tempe, AZ, USA
| [f] Department of Neurology and the Franke Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA
Correspondence:
[*]
Correspondence to: Dr. Teresa Wu, Professor, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA. E-mail: [email protected].
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.
Keywords: Alzheimer’s disease, cognition, deep learning, machine learning, neuropsychiatric symptoms
