Computational neurology: Computational modeling approaches in dementia

Autor:	Wong-Lin, KongFatt, Sanchez-Bornot, Jose M., McCombe, Niamh, Kaur, Daman, McClean, Paula L., Zou, Xin, Youssofzadeh, Vahab, Ding, Xuemei, Bucholc, Magda, Yang, Su, Prasad, Girijesh, Coyle, Damien, Maguire, Liam P., Wang, Haiying, Wang, Hui, Atiya, Nadim A. A., Joshi, Alok
Rok vydání:	2020
Předmět:	Quantitative Biology - Neurons and Cognition Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted manuscript as a book chapter in Systems Medicine: Integrative, Qualitative and Computational Approaches. Wolkenhauer, O. (ed.). Elsevier Inc
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.02214 Zobrazit plný text záznamu View this record from Arxiv