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Title: | Machine Learning for Alzheimer’s Disease and Related Dementias |
Authors: | Modat, M Bocchetta, M Dos Santos Canas, L Cash, D Ourselin, S |
Keywords: | dementia;Alzheimer’s disease;cognitive impairment;machine learning;data harmonization;biomarkers;imaging;classification;disease progression modeling |
Issue Date: | 24-Jul-2023 |
Publisher: | Humana Press |
Citation: | Modat, M. et al. (2023) 'Machine Learning for Alzheimer’s Disease and Related Dementias', in Colliot, O. (ed.) Machine Learning for Brain Disorders. (Neuromethods, vol 197). New York, NY, USA: Humana Press, pp. 807 - 846. doi: 10.1007/978-1-0716-3195-9_25. |
Series/Report no.: | Neuromethods;vol 197 |
Abstract: | Copyright © The Author(s) 2023. Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing. |
URI: | https://bura.brunel.ac.uk/handle/2438/27004 |
DOI: | https://doi.org/10.1007/978-1-0716-3195-9_25 |
ISBN: | 978-1-0716-3194-2 (pbk) 978-1-0716-3195-9 (ebk) |
Other Identifiers: | ORCID iD: Martina Bocchetta https://orcid.org/0000-0003-1814-5024 25 |
Appears in Collections: | Dept of Life Sciences Research Papers |
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