Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer's disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is becoming necessary - Computational Neurology. We provide a focused review of some computational approaches that have been developed and applied to the study of dementia, particularly Alzheimer's disease. Both mechanistic modeling and data-drive, including AI or machine learning, approaches are discussed. Linkage to clinical decision support systems for dementia diagnosis will also be discussed.
@article{arxiv.2005.02214,
title = {Computational neurology: Computational modeling approaches in dementia},
author = {KongFatt Wong-Lin and Jose M. Sanchez-Bornot and Niamh McCombe and Daman Kaur and Paula L. McClean and Xin Zou and Vahab Youssofzadeh and Xuemei Ding and Magda Bucholc and Su Yang and Girijesh Prasad and Damien Coyle and Liam P. Maguire and Haiying Wang and Hui Wang and Nadim A. A. Atiya and Alok Joshi},
journal= {arXiv preprint arXiv:2005.02214},
year = {2020}
}
Comments
Accepted manuscript as a book chapter in Systems Medicine: Integrative, Qualitative and Computational Approaches. Wolkenhauer, O. (ed.). Elsevier Inc