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dc.contributor.authorTsang, Gavin
dc.contributor.authorWildish, Deborah
dc.contributor.authorZhou, Shang-Ming
dc.date.accessioned2021-11-05T11:53:11Z
dc.date.available2021-11-05T11:53:11Z
dc.date.issued2020
dc.identifier.issn1937-3333
dc.identifier.issn1941-1189
dc.identifier.urihttp://hdl.handle.net/10026.1/18231
dc.description.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 effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

dc.format.extent113-129
dc.format.mediumPrint-Electronic
dc.languageeng
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectAlzheimer's disease
dc.subjectcognitive assessment
dc.subjectdeep learning
dc.subjectdementia
dc.subjectelectronic medical records
dc.subjecthealth informatics
dc.subjectmachine learning
dc.subjectneuroimaging
dc.subjectNLP
dc.titleHarnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges
dc.typejournal-article
dc.typeJournal Article
dc.typeResearch Support, Non-U.S. Gov't
dc.typeReview
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000722890600007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume13
plymouth.publication-statusPublished
plymouth.journalIEEE Reviews in Biomedical Engineering
dc.identifier.doi10.1109/rbme.2019.2904488
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeUnited States
dc.identifier.eissn1941-1189
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1109/rbme.2019.2904488
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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