Journal article 1063 views 791 downloads
Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
Gavin Tsang,
Xianghua Xie 
,
Shangming Zhou,
Shang-ming Zhou
,
Shangming Zhou,
Shang-ming Zhou
IEEE Reviews in Biomedical Engineering, Pages: 1 - 1
Swansea University Authors:
Xianghua Xie , Shang-ming Zhou
-
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DOI (Published version): 10.1109/RBME.2019.2904488
Abstract
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing f...
| Published in: | IEEE Reviews in Biomedical Engineering |
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| ISSN: | 1937-3333 1941-1189 |
| Published: |
IEEE
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa49119 |
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| Abstract: |
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records etc. Making the best use of these diverse and strategic resources will lead to high quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less efforts have been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate the future potentials and research directions of applying advanced machine learning, such as deep learning, to dementia informatics. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
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