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Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges / Gavin Tsang; Xianghua Xie; Shangming Zhou; Shang-ming Zhou

IEEE Reviews in Biomedical Engineering, Pages: 1 - 1

Swansea University Authors: Xianghua, Xie, Shang-ming, Zhou

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...

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Published in: IEEE Reviews in Biomedical Engineering
ISSN: 1937-3333 1941-1189
Published: IEEE 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa49119
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first_indexed 2019-03-05T14:02:13Z
last_indexed 2019-03-21T13:58:10Z
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spelling 2019-03-21T10:04:24.5104759 v2 49119 2019-03-05 Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2019-03-05 SCS 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. Journal Article IEEE Reviews in Biomedical Engineering 1 1 IEEE 1937-3333 1941-1189 31 12 2019 2019-12-31 10.1109/RBME.2019.2904488 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-03-21T10:04:24.5104759 2019-03-05T12:31:08.5697494 College of Science Computer Science Gavin Tsang 1 Xianghua Xie 0000-0002-2701-8660 2 Shangming Zhou 3 Shang-ming Zhou 0000-0002-0719-9353 4 0049119-05032019123200.pdf paperv12.pdf 2019-03-05T12:32:00.1800000 Output 5433992 application/pdf Accepted Manuscript true 2020-03-05T00:00:00.0000000 true eng
title Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
spellingShingle Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
Xianghua, Xie
Shang-ming, Zhou
title_short Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
title_full Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
title_fullStr Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
title_full_unstemmed Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
title_sort Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
118578a62021ba8ef61398da0a8750da_***_Shang-ming, Zhou
author Xianghua, Xie
Shang-ming, Zhou
author2 Gavin Tsang
Xianghua Xie
Shangming Zhou
Shang-ming Zhou
format Journal article
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publishDate 2019
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1941-1189
doi_str_mv 10.1109/RBME.2019.2904488
publisher IEEE
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hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description 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.
published_date 2019-12-31T04:10:41Z
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